Who is a Data Analyst?

A data analyst is a professional who collects, analyzes, and interprets large sets of data to identify patterns and trends, and then uses this information to make informed decisions and predictions. Data analysts may work in a variety of industries, including business, finance, healthcare, and government. They often use a variety of tools and technologies, such as spreadsheets, databases, and statistical software, to analyze data. The role of a data analyst can vary depending on the specific organization or industry they are working in, but they generally have a strong understanding of statistics and data management.

How do I become a Data Analyst in 2023?

Here's a Roadmap with downloadable learning resources for beginning with your Data Analyst Journey in 2023:

Step 1: Learn Python

Python is a high-level, interpreted, general-purpose programming language. It was created in the late 1980s and is known for its readability, easy-to-learn syntax, and dynamic typing. Python is used for various applications such as web development, data analysis, artificial intelligence, and more.

Answering the big question - Why should you care to learn Python to become a Data Analyst?

1. Versatility: Python is widely used for various tasks in data analysis, including data cleaning, visualization, and statistical modeling.
2. Large community and libraries: There is a large and active community of Python developers, which means there are many libraries and tools available for data analysis, such as NumPy, Pandas, and Matplotlib.
3. Readability: Python has a clean, readable syntax, which makes it easy to learn and write code, even for those new to programming.

4. Demand: Python is a highly sought-after skill in the job market, especially for data analysis roles.
5. Integration: Python can easily integrate with other technologies, making it a popular choice for data analysis in conjunction with databases, machine learning models, and more.

Resources to learn Python:

1. FreeCodeCamp's Python for Beginners 4hr YouTube Course

From this one single video, you will learn the fundamentals of Python and code two Python programs line-by-line. No previous programming experience is necessary before watching this course.

This course can be viewed for free on YouTube:

2. Udemy [PAID]

Udemy is an online learning platform that offers courses on Python, including courses on data analysis, web development, and more. You can browse Udemy for learning Python and take up a course on Python. Just make sure that the course has good reviews and teaches a few libraries of Python useful for data science field (examples include Numpy, Pandas, Matplotlib.

Step 2: Learn Statistics

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It helps us to make sense of large and complex data sets, and to draw conclusions and make decisions based on the data.

Resources to learn Statistics:

1. Think Stats Book for programmers [PDF DOWNLOAD LINK]

by Allen B. Downey, published by O'Reilly Media.

This book emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.

If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.

Most introductory books don't cover Bayesian statistics, but Think Stats is based on the idea that Bayesian methods are too important to postpone. By taking advantage of the PMF and CDF libraries, it is possible for beginners to learn the concepts and solve challenging problems.

This book is under the Creative Commons Attribution-NonCommercial 3.0 Unported License, which means that you are free to copy, distribute, and modify it, as long as you attribute the work and don't use it for commercial purposes.

Read this book online or Download it in PDF format or Order Think Stats from Amazon.com.

2. Statistics 101 book by David Borman

Whether you are a student looking to supplement your learning, a worker hoping to better understand how statistics works for your job, or a lifelong learner looking to improve your grasp of the world, Statistics 101 has you covered.

It is A comprehensive guide to statistics with information on collecting, measuring, analyzing, and presenting statistical data.

Unfortunately, most statistics text books just make us want to take a snooze, but with Statistics 101, you’ll learn the basics of statistics in a way that is both easy-to-understand and apply. From learning the theory of probability and different kinds of distribution concepts, to identifying data patterns and graphing and presenting precise findings, this essential guide can help turn statistical math from scary and complicated, to easy and fun.

This book can be bought online from Amazon.com and [India] Amazon.in.

3. Statistics for Data Science by Great Learning YouTube [LINK]

A Massive 7 hour YouTube video by Dr. Abhinanda Sarkar Ph.D. (Stanford) on a full course of 7 hours.

Video Description: One of the most critical aspects of the data science approach is our perception of getting the information processed. In developing insights from our accumulated data, we dig out the possibilities. And those possibilities are known as statistical analysis in Data science. Statistics acts as a tool to gather, extract, analyze, and review data, which is an input to Data science techniques; hence, learning statistics is a baby step toward becoming a data scientist. Great Learning‘s Statistics for Data Science course is for beginners and professionals who want to upgrade their skills in data science domains and learn everything about statistical analysis.

Step 3: Learn Python specifics of Data Analytics

After you've learnt all the Python basics, you should understand that Python is a very versatile programming language and you are now ready to dive deep into the world of data science. For the same reason, now you'll be focusing on the specifics of data science using Python.

Best Resources to dive deep:

1. Python for Data Analysis, 3E (book) by Wes McKinney [READ ONLINE HERE]

The third edition of this super-amazing book has been updated for pandas 1.4.0 and Python 3.10 which is the latest version of Python as of writing this article. The book contains a lot of information on the specifics you need and will help you get all the requisite tools and skills required to be a successful Data Analyst in 2023.

2. Intro to Data Analytics [FREE COURSE] by Udacity [LINK]

This course will introduce you to the world of data analysis. You'll learn how to go through the entire data analysis process, which includes:

Posing a question
Wrangling your data into a format you can use and fixing any problems with it
Exploring the data, finding patterns in it, and building your intuition about it
Drawing conclusions and/or making predictions
Communicating your findings

You'll also learn how to use the Python libraries NumPy, Pandas, and Matplotlib to write code that's cleaner, more concise, and runs faster.

Next Steps:

It's time to wrap up this pretty detailed roadmap article, but your becoming an awesome Data Analyst wouldn't stop just here.

By this point you'll be able to lookup credible resources on the web and start making projects to shine on your resume.

You'll also need to get familiar with the tools and technologies helpful for data science i.e:

How to become a Data Analyst in 2023 [Detailed Beginner's Roadmap with learning resources!]