data analyst presentation reddit

• Software Development
• Data Science and Business Analytics
• Press Announcements

Data Analyst Roadmap 2024: Step-by-Step Guide

#ezw_tco-2 .ez-toc-title{ font-size: 120%; font-weight: 500; color: #000; } #ezw_tco-2 .ez-toc-widget-container ul.ez-toc-list li.active{ background-color: #ededed; } Contents

In today’s data-driven world, data analysis is an essential aspect of problem-solving and decision-making. The demand for skilled data analysts is only going to increase as more and more organizations gather vast volumes of data.  

You also wish to work as a data analyst. After much research, you’ve decided that this is the career for you. Also, you may have heard about this job title frequently and are interested in finding out more.  In any case, you’re curious about the specifics of a career as a data analyst and, more crucially, how to go about pursuing one.

Well, you have come to the right place. If you’re looking for a structured, guided path to becoming a successful data analyst, Scaler’s Data Science Course offers a comprehensive curriculum, mentorship from industry experts, and hands-on experience with real-world data projects. This step-by-step guide will provide you with a complete data analyst roadmap – a comprehensive route for becoming a data analyst

What is Data Analysis?

Data analysis is the process of reviewing, cleansing, and organizing data to find patterns and trends. Better decisions can be made with this knowledge in a variety of industries, including marketing, technology, healthcare, and finance.

Here’s how it works:

• Cleaning Up the Data: Analysts begin by correcting any mistakes or discrepancies in the data. This guarantees the accuracy of the outcomes.
• Shaping the Data: Next, they convert the unprocessed data into more easily analyzed format, such as tables or charts.
• Modeling the Data: Lastly, they generate predictions and find patterns in the data by using models and formulas.

Who is a Data Analyst?

A data analyst is a professional skilled in processing and performing statistical analyses on data. They extract meaningful insights from data sets, translate numbers and data into plain English, and help organizations make informed decisions based on data.

Some of the key responsibilities of a data analyst: 

• Collecting and organizing data from various sources.
• Conducting thorough data analysis to identify trends, patterns, and outliers.
• Developing and implementing data models and statistical algorithms.
• Creating visualizations and reports to present findings.
• Collaborating with stakeholders to understand their data needs.

How Does This Data Analyst Roadmap Help You?

This Data Analyst Roadmap, curated by industry experts, offers a structured approach to learning, guiding you through essential skills and concepts in a logical progression. It helps you build a strong foundation, advance through key topics, and tackle complex challenges with confidence. By following this roadmap, you’ll gain a comprehensive understanding of data analysis, equipping you with the knowledge and skills to excel in the field and adapt to its evolving demands.

Step-By-Step Data Analyst Roadmap

The Step-By-Step Data Analyst Roadmap provides a structured learning path, guiding you from foundational concepts to advanced analytics techniques. Typically, completing this roadmap can take anywhere from a few months to over a year, depending on your starting skill level, learning pace, and the depth of knowledge you aim to achieve. This roadmap ensures you gain a comprehensive and practical understanding of data analysis, preparing you for a successful career in the field.

Step 1. Mathematics and Statistics

For data analysis, a strong foundation in mathematics and statistics is required. Data analysis is based on concepts like probability, descriptive statistics, regression analysis, and hypothesis testing. Become familiar with these concepts and how they are used. Online courses, textbooks, and educational resources can provide comprehensive coverage of these topics. 

1.1 Algebra

Refresh your memory on the basic algebraic operations like multiplication, division, addition, and subtraction. You’ll also encounter concepts like equations, inequalities, and functions.

1.2 Calculus

While not always necessary, knowing integrals and derivatives can be useful for advanced data analysis techniques.

1.3 Linear Algebra

Learn about linear transformations, matrices, and vectors in linear algebra. These concepts are essential for tasks like dimensional reduction and data manipulation.

1.4 Probability

Grasp the concepts of probability distributions, random variables, and how to calculate the likelihood of events.

1.5 Discrete Mathematics

Learn about the topics that are helpful for analyzing relationships within data, such as set theory, logic, and graph theory.

1.6 Descriptive Statistics

Learn how to use statistics such as mean, median, mode, and standard deviation to summarize and describe data sets.

1.7 Inferential Statistics

Move beyond describing data to drawing conclusions about populations based on samples. Confidence intervals and hypothesis testing are involved in this.

1.8 Probability Distributions

Understand various types of probability distributions (e.g., normal, binomial) and how they model real-world data.

1.9 Regression Analysis

Discover how to use methods such as linear regression to model relationships between variables.

Step 2. Data Exploration using Excel 

Guess what? You already have powerful tools for data exploration: Microsoft Excel! Excel is a widely used spreadsheet tool that offers various functions and features for data exploration and analysis. It lets you clean, organize, and understand data before diving deeper. Here are some important Excel skills to master for data exploration- 

• Making and managing worksheets
• Master important formulas such as Count, Min/Max, and Average.
• Use advanced functions such as Concatenate, SumIf, CountIf, SumProduct, and Vlookup, etc.
• Pivot tables / Conditional formatting to summarize and analyze data
• Different kinds of Charts to show off your findings
• Use sensitivity Analysis to understand how changes in variables affect your data.
• Building Gantt Chart / Financial Statement

Later, as you explore larger datasets, you’ll encounter SQL, a powerful tool for interacting with and manipulating data stored in databases. 

Step 3. Mastering Other Data Analysis Tools

Excel is just one of the tools used in data analysis. Explore other tools such as Tableau, Power BI, and Google Data Studio. These tools offer advanced visualization capabilities and interactive dashboards for data exploration. Learn how to connect to different data sources, create meaningful visualizations, and share insights with stakeholders.

Power BI is a Microsoft tool that specializes in business analytics, allowing users to create reports and dashboards with interactive visualizations. It connects to various data sources and offers real-time insights.

Tableau is renowned for its powerful data visualization capabilities, enabling users to create interactive and shareable dashboards. It’s user-friendly and supports various data sources.

Google Data Studio

Google Data Studio offers free tools to convert your data into customizable informative reports and dashboards. It integrates with other Google services, providing a seamless experience.

While these tools are essential for data analysis, gaining expertise in them can be challenging without proper guidance. Scaler’s Data Science Course provides comprehensive training on Tableau, Power BI, Google Data Studio, and other industry-standard tools, ensuring you develop the skills needed to effectively analyze and visualize data.

Step 4. Learn SQL

Structured Query Language (SQL) is a programming language used for managing and manipulating relational databases. It is a crucial skill for data analysts, as it allows you to extract, filter, and aggregate data efficiently. Learn SQL syntax, database design principles, and how to write complex queries. Practice working with real-world datasets to enhance your SQL skills. Here are some essential topics to consider- 

• Essential Queries (SELECT, WHERE, ORDER BY)
• Data Manipulation (UPDATE, DELETE, GROUP BY)
• Advanced Queries (Filtering with AND/OR, Aggregation Functions)
• Joins (INNER, LEFT, RIGHT, Self Joins)
• Subqueries (Unlocking Complex Data Relationships)
• Database Management (Creating & Modifying Tables)
• Introduction to SQL Functions (Window Functions, String Manipulation)
• Stored Procedures (Optional: Automate Complex Tasks)

Step 5. Learn Any Programming Language

While Excel and SQL are powerful tools, learning a programming language expands your capabilities as a data analyst. Choose a language such as Python, R, or Julia, and learn its syntax, data structures, and libraries for data analysis. Here are some popular options to consider:

Python is highly recommended due to its simplicity, versatility, and extensive libraries specifically designed for data analysis. It’s a popular language in data science that is versatile and easy for beginners to learn. It provides robust libraries like NumPy for numerical computation and Pandas for data manipulation.

5.2 R Language

R language is specifically designed for statistical computing and data visualization. has an extensive ecosystem of packages for doing statistical analysis and creating eye-catching visuals.

Matlab is a powerful tool for numerical computations that is widely utilized in the engineering and scientific fields. It provides advanced mathematical functions and graphical capabilities.

5.4 Python Vs. R, Which one to choose?

Choosing between Python and R depends on your specific data analysis needs. Python is versatile, with extensive libraries for data analysis and machine learning, making it suitable for a broad range of applications. R is renowned for statistical analysis and data visualization, often preferred in academia and research. Consider your project requirements, the community and support available, and your personal or team’s familiarity with the language when making your choice. Better to learn both languages if you are getting started. 

Step 6. Learn Python Libraries for Data Analysis

Now that you’ve understood the Python basics, it’s time to explore powerful Python libraries for data analysis. Python offers several libraries that enhance data analysis capabilities. Here are some essential Python libraries for data analysis-

Pandas is a library providing high-level data structures and tools for effective data manipulation and analysis. It’s essential for handling structured data.

6.2 Numpy and Scipy

Numpy offers comprehensive mathematical functions, array objects, and tools for working with arrays. Scipy builds on Numpy, providing modules for optimization, linear algebra, integration, and more. Start exploring: the Numpy website and Scipy website.

6.3  Matplotlib

Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python.

6.4  Scikit Learn

Scikit Learn is used for machine learning, providing simple and efficient tools for data analysis and modeling.

6.5 Seaborn

Seaborn is a library for making statistical graphics in Python, built on top of Matplotlib and closely integrated with Pandas data structures.

Step 7. Other Important Data Analytics Skills To Learn

Beyond core technical skills, data analysts need to master additional competencies to effectively analyze and interpret data, communicate findings, and make informed decisions.

Some other skills to learn are – 

7.1 Exploratory Data Analysis (EDA)

Exploratory Data Analysis is a critical step in the data analytics process, allowing analysts to understand the data’s distributions, main characteristics, and underlying patterns. It involves using statistical graphics and other data visualization methods to summarize the data’s essence without making assumptions.

7.2 Time Series Analysis

Time Series Analysis is essential for analyzing data points indexed in time order. It’s vital for understanding trends, seasonality, and cyclical patterns in data, helping predict future values based on past data, crucial in financial, weather forecasting, and inventory studies.

7.3 Big Data Analysis

Big Data Analysis involves extracting valuable information from large, complex data sets. Analysts need to manage, process, and analyze these vast amounts of data efficiently, often requiring knowledge of specific tools and frameworks designed for big data environments.

7.4  Advanced Data Visualization

Advanced Data Visualization transcends basic graphs, employing sophisticated techniques to illustrate complex relationships and trends in data. It enhances the interpretability of data, facilitating better decision-making by providing clear, impactful visual narratives.

7.5  Project Management Skills

Project management is key for data analysts to effectively plan, execute, and close projects. These skills ensure that data projects meet objectives, are delivered on time, and within budget, requiring adept coordination of resources, data, and stakeholder expectations.

7.6  Domain-Specific Knowledge

Domain-specific knowledge allows data analysts to apply their technical skills within a specific context, enhancing the relevance and impact of their analysis. Understanding industry-specific challenges and data nuances enables more accurate, insightful analytics.

7.7 Soft Skills Development

Soft skills like communication, teamwork, and critical thinking are indispensable for data analysts. They foster effective collaboration, clear presentation of findings, and the ability to translate complex data insights into actionable business strategies.

If you’re looking to build a strong foundation in all these essential data analysis skills, from technical expertise to soft skills mastery, Scaler’s Data Science Course offers a comprehensive curriculum designed to prepare you for a successful career in data.

Step 8. Continuous Learning and Networking

Data analysis is a constantly evolving field, with new tools, techniques, and technologies emerging regularly. 

• Stay updated with the latest trends and advancements by attending webinars, conferences, and workshops.
• Engage with the data analysis community through online forums such as Cross Validated on Stack Exchange , social media groups, and professional networking events or platforms like LinkedIn. 
• Collaborate with fellow analysts, share knowledge, and learn from their experiences. Continuous learning and networking will help you stay ahead in this dynamic field.

Final Step: Case Studies, Project, Portfolio, and ATS Resume

Learning is just the first step. Now it’s time to showcase your data analysis skills! Here’s how:

Real-world case studies : Apply your skills to real-world case studies. Case studies let you apply your skills to solve problems faced by businesses. Websites like Kaggle offer a plethora of case studies across various industries.

Projects : Work on data analysis projects to demonstrate your competence as a data analyst. Explore diverse datasets, solve complex problems, and present your findings effectively. You can work on the following data analysis projects- 

• Color Detection Project
• Exploratory Data Analysis Projects (EDA)
• Fake News Detection
• Sales Forecasting Analysis
• Customer Segmentation Analysis
• Sentiment Analysis
• Social Media Reputation Monitoring

Use platforms like GitHub to host and showcase your project work.

Strong online presence : Build an effective portfolio on platforms like LinkedIn showcasing your work, including visualizations, reports, and analysis. Highlight your data analysis projects. Tableau Public provides examples and inspiration.

ATS-Friendly Resume : Moreover, optimize your resume for Applicant Tracking Systems (ATS) by incorporating relevant keywords, highlighting your skills and accomplishments, and tailoring it to specific job descriptions.

Data Analyst Career Opportunities With Salary

There is a huge demand for qualified data analysts. In fact, the Bureau of Labor Statistics projects a 30% job growth for data analysts and statisticians between 2022 and 2032 , which is much faster than the average for all occupations. The growing volume of data produced by companies and organizations of all sizes is the main cause of this quick expansion.

However, data analysts typically earn competitive salaries. Data analysts may expect to make between $62,000 and $96,000 annually on average in the United States, and between ₹4L and ₹10L annually in India, according to Glassdoor.   Industry, location, and level of expertise all affect data analyst salaries. Here are some average base salaries for some of the top data analyst job roles according to Glassdoor: 

For people who enjoy working with data and solving problems, a career in data analysis is an excellent choice because of its strong job demand and significant income potential.

What’s Next? How to Start Your Career in Data Analytics?

So, you’re interested in becoming a data analyst? That’s very impressive! There are several options to get you started, which is fantastic news. Here are some routes to consider that are flexible enough to fit your needs-

• Formal Education : Enrolling in a data science boot camp or a degree program in statistics, computer science, or a related field can provide a more structured learning environment. 
• Self-learning : This route allows you to learn at your own pace and explore areas that particularly interest you. Here are some resources to get you going:
• Books: There are many great books available on data analysis. A good starting point might be "Data Analytics using Python" or "Numsense! Data Science for the Layman" .
• Online Communities : Joining online communities like Kaggle and LinkedIn allows you to connect with other aspiring and experienced data analysts. You can participate in discussions, ask questions, and even find datasets to practice your skills on.
• Consider Specialized Certifications or Courses : While not mandatory, pursuing data analysis certifications and courses can validate your skills and enhance your resume. Many free and paid online courses can teach you data analysis skills. Scaler’s Data Science Course offers a comprehensive and immersive boot camp experience designed to equip you with the practical skills and industry knowledge needed to launch your data analyst career.

The journey to becoming a successful data analyst requires dedication, continuous learning, and the right skill set. Following this step-by-step roadmap will provide you with a comprehensive guide to developing the necessary skills and knowledge. Embrace the data-driven era and equip yourself with the tools and techniques to excel as a data analyst in 2024 and beyond.

Read These Important Roadmaps: More Paths to Career Success

Frequently Asked Questions

Can we become a data analyst in 3 months.

Becoming a proficient data analyst in just three months is ambitious but achievable with a focused and dedicated approach. However, the learning curve may vary depending on your prior knowledge, educational background, and the amount of time you can commit to learning each day. It is essential to follow a structured learning path, practice regularly, and work on real-world projects to gain hands-on experience.

How much SQL is needed for data analysts?

SQL is a fundamental skill for data analysts. A strong understanding of SQL syntax, the ability to write complex queries, and proficiency in tasks such as data manipulation and extraction are crucial for success in the field. Regular practice and working with real-world datasets will enable you to deepen your SQL skills and handle various data-related challenges.

Is data analyst still in demand in 2024 and beyond?

Absolutely! The demand for skilled data analysts continues to grow as organizations across industries recognize the value of data-driven decision-making. According to the Bureau of Labor Statistics, the employment of data analysts is projected to grow much faster than the average for all occupations. As technology advances and more businesses aim to leverage data for gaining insights, the demand for skilled data analysts is expected to remain high in 2024 and beyond.

Should I learn SQL or Tableau first?

Both SQL and Tableau play crucial roles in data analysis, but they serve different purposes. SQL is a programming language that allows you to extract, manipulate, and analyze data from databases. Tableau, on the other hand, is a data visualization tool that helps in creating interactive dashboards and visualizations. It is recommended to learn SQL first, as it provides a solid foundation for data manipulation and extraction, which are essential steps before visualizing data using Tableau.

Can I transition from a non-tech background to become a data analyst?

Absolutely! A non-technical background should not stop you from pursuing a career as a data analyst. While a strong foundation in mathematics and statistics is beneficial, you can acquire the necessary technical skills through online courses, boot camps, and self-study. With dedication, practice, and a passion for data analysis, you can successfully transition to a data analyst role and thrive in the field.

I’m in the United States not India. This form won’t allow me to identify myself without the OTP, but it’s sending it to the wrong phone number.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Get Free Career Counselling

By continuing, I have read and agree to Scaler’s  Terms  and  Privacy Policy

Get Free Career Counselling ➞

• Data Science
• Data Analysis
• Data Visualization
• Machine Learning
• Deep Learning
• Computer Vision
• Artificial Intelligence
• AI ML DS Interview Series
• AI ML DS Projects series
• Data Engineering
• Web Scrapping

Data Analyst Roadmap 2024 – A Complete Guide

Dreaming of a career where you unlock the secrets hidden within data and drive informed business decisions? Becoming a data analyst could be your perfect path! This comprehensive Data Analyst Roadmap for beginners unveils everything you need to know about navigating this exciting field, including essential data analyst skills. Whether you’re a complete beginner or looking to transition from another role, we’ll guide you through the roadmap for data analysts, including essential skills, educational paths, and tools you’ll need to master to become a data analyst. Explore practical project ideas, conquer job search strategies, and discover the salary potential that awaits a skilled data analyst. Dive in and prepare to transform your future – your data-driven journey starts here!

Data analyst demand is skyrocketing! This booming field offers amazing salaries, and growth, and welcomes diverse backgrounds. Ready to join the hottest IT trend? Our step-by-step Data Analyst Course Roadma p equips you with the essentials to launch your data analyst career fast. Don’t wait – land your dream job today!

Did you know Bengaluru ranks among the Top 3 global data analyst hubs? Have a look at the list:

• San Jose, California
• Bengaluru, India
• Geneva, Switzerland

Get ready to unlock exciting opportunities! Buckle up and let’s connect the dots to your Data Analyst Future.

Table of Content

What is Data Analysis?

Who is a data analyst, why data analyst, types of data analysts, how to become a data analyst : roadmap – skills required , for tech domain, for non-tech domain, best courses for data analysts, career path of a data analyst, data analyst career path, future scope of data analyst.

Join our “ Complete Machine Learning & Data Science Program “ to master data analysis, machine learning algorithms, and real-world projects. Get expert guidance and kickstart your career in data science today!

Data analysis, enriched by essential data analyst skills, is the systematic process of inspecting, cleaning, transforming, and modeling data to uncover valuable insights. These skills encompass proficiency in statistical analysis, data manipulation using tools like Python or R, and the ability to create compelling data visualizations. Data analysis leverage these capabilities to identify patterns, correlations, and trends within datasets, enabling informed decision-making across various industries. By employing techniques such as data mining and machine learning, data analysts play a pivotal role in transforming raw data into actionable insights that drive business strategies and operational efficiencies

A data analyst is a professional who collects, cleans, analyzes, and communicates insights from data. They work in various industries, helping organizations make informed decisions based on evidence. Here’s a breakdown of their responsibilities and data analyst skills:

1. Data Gathering and Cleaning:

• Data analysts collect information from various sources like surveys, website records, financial data, or scientific experiments.
• They then meticulously clean and organize the data, ensuring its accuracy and completeness before further analysis.

2. Data Analysis and Pattern Identification:

• Using statistical methods, programming languages, and data visualization tools, analysts explore and analyze the data.
• They search for patterns, trends, and anomalies, revealing hidden connections and insights within the information.

3. Insight Communication and Reporting:

• A crucial skill for data analysts is translating complex findings into clear, concise, and actionable insights that stakeholders can understand and use.
• This often involves creating reports, presentations, and dashboards that effectively communicate the data’s story.

4. Problem-Solving and Performance Optimization:

• Data analysts go beyond interpreting data; they use it to solve problems and improve processes in various contexts.
• This might involve analyzing customer behavior to optimize marketing campaigns, identifying fraudulent transactions in financial systems, or predicting equipment failures for better maintenance schedules.

Being a Data Analyst you will be working on real-life problem-solving scenarios and with this fast-paced, evolving technology, the demand for Data Analysts has grown enormously. Moving with this pace of advancement, the competition is growing every day and companies require new methods to compete for their existence and that’s what Data Analysts do. Let’s understand the Data Analysts job in 4 simple ways:

• Being a Data Analyst, you’ll be working closely with the raw data and will generate valuable insights that will help companies to decide their future goals.
• If you’re someone who likes thinking out of the box then you are the perfect fit for this domain. Data Analysts help organizations to work with both business and data closely. This eventually maximizes the output for generating more business values.
• Nevertheless, this field gives you a handsome salary for all levels of expertise. Being a Data Analyst you can earn more than $80k per annum and around 4LPA in India (for starting level).
• According to multiple reports, the demand for Data Analysts job are high VS the supply to the market is comparatively less and that’s one of the reasons why people are shifting their career to Data Science. Till now, there are more than 28,000 job postings available in India and 414,000+ jobs are available worldwide. 

There are many different types of data analysts, each specializing in a specific area or industry. Here are some of the most common types :-

• Business Intelligence Analysts: Analyze business data for insights, informed decisions, and performance improvement.
• Financial Analysts: Focus on financial data for budgeting, investments, and market trends analysis.
• Healthcare Data Analysts: Work with healthcare data for patient outcomes, operational optimization, and medical research.
• Marketing Analysts: Analyze marketing data for campaign effectiveness, consumer behavior, and market trends.
• Operations Analysts: Optimize processes by analyzing operational data, enhancing efficiency, and reducing costs.
• Sports Analysts: Analyze sports data for performance evaluation, strategy improvement, and player/team assessment.
• Crime Analysts: Analyze crime data for pattern identification, assisting law enforcement in prevention and solving.
• Environmental Data Analysts: Analyze environmental data for ecological trends, climate patterns, and human impact assessment.
• Social Media Analysts: Analyze social media data for user behavior understanding and insights for marketing strategies.
• Economic Analysts: Study economic data for trend understanding, economic condition forecasting, and policy insights.

To become a data analyst, it’s essential to develop a strong foundation in mathematics and statistics, which are fundamental data analyst skills for understanding and interpreting complex datasets. Additionally, proficiency in programming languages like Python , R , or SQL is crucial for manipulating data and conducting statistical analysis. Practical experience gained through real-world projects and certifications can further enhance your skills and make you more competitive in the job market. Continuous learning and staying updated with the latest trends and technologies in data analysis are also key to success in this dynamic and evolving field.

1. Mathematics and Statistics

Mathematics

• Standard Deviation
• System of Linear Equation
• Solving Linear Equations using Gaussian Elimination
• Row Echelon Form
• Matrix Approximation
• Vector Operations
• Linear Mappings
• Linear Algebra
• Probability
• Mean, Standard Deviation, and Variance — Implementation
• Descriptive and Inferential Statistics
• Probability Theory and Distribution
• Sampling Distribution
• Linear Regression
• Sample Error and True Error
• Bias Vs Variance and Its Trade-Off
• Hypothesis Testing
• Confidence Intervals
• Correlation and Covariance
• Correlation Coefficient
• Covariance Matrix
• Pearson Correlation
• Spearman’s Rank Correlation Measure
• Kendall Rank Correlation Measure
• Robust Correlations

Programming Languages

• R Programming

Learning Edge – Also refer to the below-mentioned articles to get the full insight: How to Learn Python in 21 Days? 30 Day of SQL – From Basic to Advanced Level

Required Skills for Data Analysts (Basic)

• Problem Solving
• Database Knowledge
• Data Collection
• Data Cleaning
• Communication Skill

To learn more about Database with Python, refer to this article: Python Database Tutorial

Required Tools for Data Analysis (Tech)

• Domain Knowledge: Having general background knowledge of the field/environment in which you’ll be working is mandatory so that accurate methods and tools can be applied.
• Problem-Solving Skill: A data analyst must always be prepared to perform troubleshooting when any issue arises that’s why problem-solving skills become crucial while analyzing data.
• Computer Skills: At any point in time, any query can arise and to figure that out you must know to find out the solution so carrying basic skills related to computers is a ‘must to have’ skill.
• Dashboarding: To properly analyze any given set of data would require you to perform dashboarding. It helps in bringing all data together and displays all key metrics and insights.

Required Tools for Data Analysis (Non-Tech)

To make a strong grip in this field will require you to have hands-on practice in some of the most crucial skills, which we’re mentioning below for the best reference:

• Senior Data Scientist 
• VP / Director
• Chief Data Officer / Chief Data Scientist
• Senior Business Analyst
• Analytics / Engagement Manager
• Head of Analytics
• Engagement Manager
• Portfolio Manager
• Group Finance Manager

Today, Billions of companies are generating data on a daily basis and using it to make crucial business decisions. It helps in deciding their future goals and setting new milestones. We’re living in a world where Data is the new fuel and to make it useful data analysts are required in every sector. The more data – the more requirement and thus it is projected that the market share of data analysts are likely to grow by USD 650+ Billion at a CAGR of above 13% and that’s what makes it among the most sought-after profession in the world. So, the answer is YES, it’s an excellent choice to start your career towards becoming a successful Data Analyst.

In conclusion, data analysis offers a promising career path with high demand and attractive opportunities . Bengaluru ranks among the top cities for data analyst jobs, highlighting the global need for skilled professionals in this field. Data analysts play a crucial role in collecting, analyzing, and communicating insights that drive informed business decisions. Whether you’re just starting or looking to advance your career, mastering skills in mathematics , statistics , and data tools is essential. With the right skills and experience, you can embark on a rewarding journey as a data analyst, contributing to the ever-growing importance of data in shaping the future of businesses worldwide.

Data Analyst Roadmap 2024- FAQs

How to become a data analyst.

Step guidance to Become a successful Data Analyst: Learn Programming Language first (Python, or R) Build basic skills (such as Problem-solving, DB, Data Wrangling, etc.) Start building a strong knowledge of basic tools (such as MS Excel, Tableau, etc.) Try getting hands-on practice on Real-life projects for better clarity Opt for a relevant certification/course which will add value to your portfolio Start applying for entry-level jobs to begin your journey

What does Data Analyst do?

Data Analysts are responsible for developing and managing reports to solve and project different data. Besides this, a data analyst should have the knowledge and skills to turn any provided raw data into insightful ones that can directly impact any taken business decisions.

What can I do after the 12th to become a data analyst?

If you’re looking to get into the field of Data Analyst, then you must possess a degree in Computer Science or any relevant degree in the field of Statistics, Mathematics, etc. However, it is also to be noted that the necessary candidate should possess some of the necessary skills that are required in becoming a Data Analyst, these are: Problem Solving Strong Communication Skill Knowledge of Database Data Wrangling (Collection, Cleaning & Visualization) MS Excel Tableau Power BI, etc.

What is required to become a data analyst?

To be a data analyst, master SQL, Python, and data visualization. Develop soft skills, explore education options, and build a portfolio for success in this dynamic field.

Is Python required for data analyst?

Python is crucial for data analysts, being the top programming language in demand, easy to learn, versatile, and powerful. Key tools like Jupyter Notebook and Pandas are Python-based, providing a competitive edge. Python skills also lead to higher salaries, with an average of $103,059 compared to $88,244 for those without.

Please Login to comment...

Similar reads.

• AI-ML-DS Blogs
• Data Analytics
• How to Delete Discord Servers: Step by Step Guide
• Google increases YouTube Premium price in India: Check our the latest plans
• California Lawmakers Pass Bill to Limit AI Replicas
• Best 10 IPTV Service Providers in Germany
• Content Improvement League 2024: From Good To A Great Article

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

You're reading a free article with opinions that may differ from The Motley Fool's Premium Investing Services. Become a Motley Fool member today to get instant access to our top analyst recommendations, in-depth research, investing resources, and more. Learn More

Nvidia's Big Day Is Here: What to Expect When the AI Giant Reports After the Bell

• The maker of chips for AI development will release its latest results after the closing bell today.
• Wall Street expects another bout of triple-digit percentage growth in revenue, led by the data center.
• Results will be key in determining the direction of its stock, which has soared since the start of 2023.
• Motley Fool Issues Rare “All In” Buy Alert

NASDAQ: NVDA

Investors are about to get a fresh look at Nvidia's artificial intelligence chip sales.

Nvidia ( NVDA 1.51% ) is at the epicenter of the artificial intelligence (AI) revolution. It designs the most powerful data center chips for AI development, and demand continues to outstrip supply which is driving a surge in the company's revenue and earnings.

After the closing bell today -- at around 5:00pm Eastern Time and 2:00pm Pacific -- Nvidia will release its financial results for its fiscal 2025's second quarter (ended July 31). The report will give investors a fresh look at the chip giant's sales, and what it expects for the rest of this year.

Here's what you need to know!

Image source: Nvidia.

Wall Street expects another blowout revenue number

Nvidia's official guidance points to $28 billion in total revenue for Q2, representing 107% growth compared to the year-ago period. That might be conservative, though, because Wall Street's consensus estimate has steadily climbed over the last couple of months and currently stands at $28.7 billion (according to LSEG).

Considering Nvidia delivered $26 billion in revenue during the first quarter (ended April 28), which was a whopping $2 billion more than the company had originally forecast, it's no surprise that analysts think a beat is in the cards for Q2.

The data center segment accounted for $22.6 billion of Nvidia's total Q1 revenue. All eyes will be on that number in the Q2 report because Nvidia's graphics processors ( GPU s) for the data center are critical to AI development . According to Wall Street, it could come in somewhere between $24.5 billion and $25.2 billion -- any result above the high end of that range will likely spark a fresh wave of enthusiasm in Nvidia stock.

Tech giants like Microsoft , Alphabet , and Meta Platforms (to name a few) have each committed to spending tens of billions of dollars on AI data center infrastructure this year, and a significant amount of that money will flow directly to Nvidia through GPU sales.

That includes sales of the H100 GPU which set the benchmark for the industry last year, and the more recent H200 which can perform AI inference at twice the pace of its predecessor. But there's also an entirely new generation of chips on the way.

Expect an update on Nvidia's new Blackwell chips

Earlier this year, Nvidia announced a new GPU architecture called Blackwell. It's designed to accommodate trillion-parameter large language models (LLMs) which, until now, have only been developed by leading AI companies like OpenAI .

Blackwell-based GPUs will be capable of substantially higher performance relative to their predecessors, and Nvidia says they will also be significantly more energy efficient.  For example, the new DGX B200 system combines eight Blackwell B200 GPUs and can  train AI models three times faster, performing AI inference 15 times faster, than the older DGX H100 system.

Nvidia CEO Jensen Huang says B200 GPUs will be priced at around $30,000 to $40,000, which is in the ballpark of what many customers paid for their H100 GPUs. In other words, given the substantial performance benefits, Blackwell will make it far more cost-efficient for businesses to access and deploy the most advanced AI models.

According to comments from Huang back in May, Nvidia should have started shipping Blackwell GPUs to customers in Q2, with sales ramping up as the year progresses. However, a recent report by The Information suggests a three-month delay could be due to a technical issue with the next-generation chips.

Nvidia hasn't confirmed those rumored delays, but investors should listen closely for comments on Q2 Blackwell sales and any update to its guidance for the remainder of fiscal 2025. Huang previously said he expects to see "a lot" of Blackwell revenue this year, so any variation to the company's timeline could substantially impact its future financial results.

Here's how Nvidia stock might react

Nvidia stock has soared 765% since the start of 2023, when the AI boom really started ramping up. It's now a $3.1 trillion company, so even a small move in its stock can be worth billions of dollars to its valuation.

While the performance of any stock on a given day is mostly noise, Nvidia reported its Q1 earnings after the bell on May 22, and its stock jumped 9.3% the following day. Remember, the company beat its revenue guidance by $2 billion on that occasion, so it's possible that a similar result this time around could yield a comparable increase in its stock price.

With that said, Nvidia stock is currently trading 7% below its all-time high following a steep market correction earlier this month, and if the company delivers weaker results than expected, its stock could slip into a much deeper drawdown.

However, Nvidia stock appears cheap right now for investors with a multi-year time horizon. Wall Street expects the company to deliver $0.64 in earnings per share in Q2, which will take its trailing 12-month earnings to $2.17. That places the stock at a price-to-earnings (P/E) ratio of 58.3.

Although that's almost twice as expensive as the Nasdaq-100 index, which trades at a P/E ratio of 32, the picture appears very different when looking further into the future. Analysts predict Nvidia will generate $3.81 in earnings per share in fiscal 2026, placing the stock at a more reasonable forward P/E ratio of 33.2.

In other words, as long as Nvidia's Q2 report doesn't produce a negative surprise, its stock looks like a good value at the current price for investors who can stay the course for at least the next couple of years.

Randi Zuckerberg, a former director of market development and spokeswoman for Facebook and sister to Meta Platforms CEO Mark Zuckerberg, is a member of The Motley Fool's board of directors. Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Anthony Di Pizio has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Alphabet, Meta Platforms, Microsoft, and Nvidia. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy .

Related Articles

Premium Investing Services

Invest better with The Motley Fool. Get stock recommendations, portfolio guidance, and more from The Motley Fool's premium services.

Nvidia second-quarter sales likely to double, even a slight miss may hurt shares

• Medium Text

Sign up here.

Reporting by Arsheeya Bajwa in Bengaluru; Editing by Sayantani Ghosh

Our Standards: The Thomson Reuters Trust Principles. , opens new tab

Exclusive: Intel CEO to pitch board on plans to shed assets, cut costs, source says

Intel is suffering through one of its worst periods as it tries to play catchup in the AI era against companies such as Nvidia.

Get the Reddit app

Dedicated to web analytics, data and business analytics. We're here to discuss analysis of data, learning of skills and implementation of web analytics.

Is being able to do presentations a vital aspect of analytics?

I have anxiety issues going in front of people. I know as an data/business analyst, you have to go in front of people and present your findings/insights. As someone who loathes doing this, does this mean analytics isnt for me?

By continuing, you agree to our User Agreement and acknowledge that you understand the Privacy Policy .

Enter the 6-digit code from your authenticator app

You’ve set up two-factor authentication for this account.

Enter a 6-digit backup code

Create your username and password.

Reddit is anonymous, so your username is what you’ll go by here. Choose wisely—because once you get a name, you can’t change it.

Reset your password

Enter your email address or username and we’ll send you a link to reset your password

Check your inbox

An email with a link to reset your password was sent to the email address associated with your account

Choose a Reddit account to continue

25 Terms All Aspiring Data Analysts Should Know

Every field is ring-fenced by its own specialist vocabulary. Whether its the legalize of the lawyer or the acronym-heavy shorthand of the marketer, this terminology can serve as an intimidating barrier to entry for the budding career changer.

And the field of Data Analytics is no different. If you were a fly on a wall at a meeting of data analysts, you’d overhear them discussing doing all sorts of things to their data, from mining it, to mapping it, to modeling and monitoring it. You needn’t be intimidated, though. All these terms are simple enough to understand—and in this article, we’ll prove it.

By the end, you’ll have a basic understanding of the most essential processes, tools and tasks in Data Analytics. These are all covered in detail in our fully mentored Intro to Data Analytics Course , which takes you from complete beginner to data savvy in just one month. For now though, let’s crack open the top twenty-five terms and find out what’s inside.

1. Dashboard

Data analysts use a data visualization tool, commonly known as a dashboard, to convert all the data they receive into charts and graphs.

It’s essentially their control room, and they’ve probably spent many painstaking hours constructing this data hub. Make sure to tell them it looks great.

2. Data collection

A rather broad term to describe the actual act of collecting data. Data is collected via numerous methods, depending on the nature of the business or organization.

Data might be collected from the results of an online survey, or via sensors that record the comings and goings of people entering a shopping centre. A data analyst has to ensure that the data is collected securely and without encountering problems.

3. Statistics

Data analysts should have at least a rudimentary understanding of statistics, since they often play a part in the analyzing of data.

It’s important to know the difference between discrete variables and continuous ones, and data analysts will need to have a good grasp of statistical modelling. Start here: What’s the difference between descriptive and inferential statistics?

4. Data modelling

Data models are fairly difficult for the layman to get their head around. To put it simply, data models are used to map out the ways in which data needs to flow.

Using text and symbols, the relationships between complex streams of data and their movements can be understood at a more basic level. Once you establish where the data’s headed, you can begin to plot out how you plan to analyze it.

5. Data accuracy

Data accuracy, on the other hand, is a fairly simple concept. The data you gather and record must be correct, otherwise business decisions are going to be made based on false information.

Another aspect of data accuracy relates to the methods of data collection—there should be standard ways of collecting data within a business to ensure consistent data gathering.

6. Data mining

Data mining is at the heart of data analytics—broadly speaking, it refers to the whole process of searching through data to identify patterns and trends.

Data analysts operate at the coalface of the information industry.

7. Data monitoring

Data analysts are expected to routinely check the gathering and storage of data to ensure it meets standards of quality and formatting.

Good data monitoring practices will save a business time and money by avoiding having to check data before it is moved.

8. Data cleaning / data cleansing

Data cleaning is the act of removing data that will lead to distorted or inaccurate analysis.

If your data is dirty, either because you’ve collected it in a poor way, or because it contains inaccuracies, then it’s time to put on a wash – wrong decisions are going to be made down the line.

9. Predictive analysis

Also known as predictive modelling, predictive analysis involves using data to make assumptions and predictions on future outcomes.

It’s essential to use predictive analysis to maintain a competitive advantage in business.

10. Data integrity

Data integrity is the maintenance and protection of data over its entire lifecycle. It relates to security, backing up and removing duplicate data.

11. Data extraction

Data extraction is the actual process of taking data from its source with the intention of storing or processing it.

Usually the data is unstructured at the point of extraction, and can be in any form, such as tables and indexes.

12. Data validation

Data validation involves ensuring the data you gather is correct and meaningful. Data analysts need valid data, otherwise they’re nothing!

In Excel, the data validation tool involves placing rules on cells so that users inputting data are restricted with what they enter. This ensures users can’t make mistakes, and you don’t end up with invalid data.

13. Data transformation

It’s seldom the case that data extracted at the source is in the correct format for analysis. It needs to be converted to a format used by the destination system.

For example, when moving data to a cloud data warehouse, the data type typically needs to be changed.

14. Forecasting

Like predictive analysis, forecasting lies at the heart of data analysis. It’s about making decisions on the future based on past and present data.

A variety of methods are used in forecasting, and what you use depends on what kind of data you’re analyzing.

Qualitative forecasting methods are used when the data available isn’t relevant to the forecasts, whereas quantitative methods are used when handling numerical data.

15. Building a data pipeline

One of the most important aspects of data analysis is maintaining an efficient flow of data. Without enabling data to flow successfully, a data analyst can’t begin their work.

Many things can go wrong with the flow of data, and the data pipeline aims to make the route as smooth and direct as possible. It’s not without its flaws however, and data can become corrupted or become duplicated in transit.

16. Data integration

This involves unifying data from a variety of different sources.

For example, combining the databases of two companies that are merging, or when data needs to be shared with other parties.

As data becomes more and more significant in business, the process of data integration is becoming increasingly common.

17. Algorithms

Algorithms, which are step-by-step methods used to solve problems, are used throughout data analytics.

Having a good grasp of how to create algorithms in Excel is an essential part of the role of data analyst. They’re used to manipulate data, whether it be searching for a particular item, sorting items or locating certain aspects of data.

18. Mapping

Crucial to the success of many data processes, data mapping is an integral part of the larger processes of data migration and data integration.

Mapping matches fields from different data sources so that data can be moved successfully. What appears in one field, for example a telephone number, needs to be accurately replicated in the corresponding destination field.

19. Segments / segmentation

The process of segmentation involves separating and dividing up data into chunks, so that can focus on specific analysis of a certain aspect of the data.

Segmentation is essential to data analysis in marketing, so that you can focus on particular customer buying habits more closely.

20. Unstructured data

This is data that doesn’t fit in a conventional database, generally because by nature it is impossible to analyze.

More often than not, text is classified as unstructured data. Surveys, call centre transcripts and other such forms of data are examples of unstructured data.

Application Protocol Interfaces, ready-made code which automates a range of functions, are often used in data analysis. To speed up the process of predictive analysis, APIs are used to quickly crunch and digest data. We need to convert information into intelligence, and APIs makes data analysts lives easier by undertaking the often monotonous tasks associated with such work.

22. Data enrichment

When speaking about customer data, the process of data enrichment relates to the merging of third-party data with existing data.

The term “enrichment” relates to the fact that your raw data becomes much more valuable when you add extra data to it. It’s all to do with knowing more about your customers, and when equipped with such knowledge brands are able to personalise their marketing.

23. Data accessibility

By improving the accessibility of your data, other stakeholders are able to use such data to influence their own decisions. The more knowledge a company’s employees are about their data, the better equipped they are to make informed decisions, thus staying ahead of the competition.

24. Data reconciliation

Data reconciliation is essentially a check that is made to ensure a data migration is functioning correctly. The target data is compared against the original source data to check everything is going to plan. It’s important to demonstrate that the migration is not encountering problems.

25. Data standardization

In order to allow disparate sets of data to be used together, a common form has to be determined. It’s essentially the act of putting different variables on the same scale, so that they can be compared and contrasted. This process happens after the data is taken from the source and before it is loaded into the target systems.

So what do you think? All these terms and processes may seem daunting at first, but if any of them have piqued your interest, why not try this free introductory data analytics short course?  Aside from this post, we’ve also covered  the differences between data analysts and data scientists , while our data analyst salary guide will give you an idea of what you can earn as a data analyst.