Best Online resources to learn Data Analysis
Are you into the various uses and possibilities of data science that helps to create new innovations and technologies each day that changes the world into a more advanced place everyday? If yes, you might be interested in knowing the best online resources to learn data analysis.
As we all know, the various advancements in technologies that we see today in different fields or professions like medical and health care sectors, automobile industries, e-commerce sites, finance and trading, and many more have one thing in common associated with them, and it’s data science and analysis.
Because of its wide scope of uses, it is regarded as one of the highest paying as well as demanding jobs ever in this era.
Companies like Facebook, Twitter, Linkedin, YouTube, Amazon and many more use large amounts of databases which are impossible to assemble and sort manually without assistance from softwares.
With that being said, it’s important that we know some of the essential skills of being a data analyst to such tasks.
This article will be focusing on the same by providing insights on the skills required and the best online resources to learn data analysis.
We have collected some of the Best Online Resources to learn Data Analysis like Courses and Books. Let’s get started –
Best Online Resources to learn data analysis:
Skills Required for Data Analysis:
Programming Languages:
Without having knowledge of any particular programming languages, it is impossible to work with data analysis of any sort.
So, it is essential for a data analyst to be proficient in at least one programming language like Python, R, SAS, MATLAB, Java, SQL and many more.
Not only these, as a data analyst you need to be accustomed to working with libraries and packages like reshape2, NumPy, pandas, ggplot 2, scipy, etc. The more you know, the better you work in data analysis.
Machine Learning:
When computers are programmed in a manner that makes them independent of major human assistance, it is known as machine learning.
To simply put, machine learning is a subset of artificial intelligence that prioritizes the application of algorithms and data in a way that works by replicating how the human brain learns, works and improves.
Although it is not necessary to have knowledge on machine learning for working with data analysis, it is preferable so that you can expand and improve your knowledge and apply the best algorithms of machine learning in applications involving data analysis.
Statistics:
Data analysis is incomplete without statistics as it’s the fundamental base of understanding data analysis.
Statistics involves the collection, analysis, interpretation and presentation of data. So a data analyst should be highly proficient in working with statistics and its complexity too.
That being said, it is essential that you have your knowledge and concepts cleared on topics like statistical tests, distributions, probability, graphs, and many more.
Mathematics:
It is no secret that numbers play a vital role in data analysis. All the databases, codes, systems, etc involves the play of numbers.
Hence, as a data analyst, having your concepts clear in mathematics is very essential.
You need to have knowledge on several important mathematical topics like matrix manipulations, dot products, eigenvalues and eigenvectors, multivariate calculus, linear algebra, multivariable derivatives, and many more.
Data Wrangling:
The process of cleaning and combining unstructured as well as complex datasets for convenient analysis and access is known as data wrangling.
This is essential in data analysis as the number and amount of data along with their sources continuously increases with time, making it compulsory to analyze large sets of data altogether to save time.
It involves manual conversion and mapping of data of a particular format to a different format for reliable assembling and consumption of the data. So it’s important that you have your concepts cleared on databases like SQL and NoSQL based systems.
Along with that, you need to know about Hadoop, Spark, MongoDB as well as Oracle, PostgreSQL, MySQL and Netezza.
Data Visualization:
The ability to show or illustrate data findings or information with the help of illustrations and graphical representation is known as data visualization.
The main objective of data visualization allows the users to get a clear picture and understanding of various data-based insights.
Even users who don’t have a formal training in data analysis can understand such insights.
In order to work with data visualization, you need to have knowledge on working with visualization tools such as matplotlib, ggplot, D3.js., Seaborn as well as reporting tools like power bi, Tableau and many more.
With the help of data visualization, data scientists and analysts contribute in breaking down or working with complex ideas along with its various patterns that further empowers a decision maker to make the perfect decision for the business based on the same.
Best Online Courses:
Data Analysis Courses:
- Become a Data Analyst – Udacity
- Data Analyst with R Programming – Edureka
- Mastering Data Analysis with Python Pandas – Educative.io
- Data Analyst with R – Datacamp
- Data Analyst with Python – Datacamp
- Data Analyst Masters Program – Edureka
- Data Science Specialization – Johns Hopkins University
- IBM Data Science Professional Certificate – IBM
- Data Science Foundations using R Specialization – John Hopkins University
- Applied Data Science with Python Specialization – University of Michigan
- IBM Data Analyst Professional Certificate – IBM
- Google Data Analytics Professional Certificate – Google
Programming Courses:
- Programming for Data Science with Python – Udacity
- Intermediate Python – Udacity
- Programming for Data Science with R – Udacity
- Online Python Course – Edureka
- R Programming – Datacamp
- Python Programmer – Datacamp
- Python for Everybody Specialization – University of Michigan
- Crash course on Python – Google
- Python 3 Programming Specialization – University of Michigan
- R Programming Course – Johns Hopkins University
- Python for Beginners – Udemy
- R Programming A-Z for data Science with Real Examples – Udemy
- Learn R – Codecademy
Machine Learning Courses:
- Intro to machine learning with Tensorflow – Udacity
- Become a Machine Learning Engineer – Udacity
- Machine Learning Masters Program – Edureka
- Machine Learning Course – Stanford University
- Machine Learning with Python – IBM
- Advanced Machine Learning Specialization – HSE University
- Machine Learning for All – University of London
- Machine Learning A – Z Hands on Python and R in Data Science – Udemy
- Python for Data Science and Machine Learning Bootcamp Udemy
Statistics Courses:
- Intro to descriptive Statistics – Udacity
- Intro to Inferential Statistics – Udacity
- Intro to statistics – Udacity
- Statistics with Python Specialization – University of Michigan
- Statistics with R Specialization – Duke University
- Advanced Statistics for Data Science Specialization – Johns Hopkins University
- Basic Statistics – University of Amsterdam
- Data Science: Statistics and Machine Learning – Johns Hopkins University
- Statistics for Data Science and Business Analysis – Udemy
- Learn Statistics with R – Codecademy
- Learn Statistics with Python – Codecademy
- Master Statistics with Python – Codecademy
Mathematics Courses:
- Mathematics for Machine Learning Specialization – Imperial College London
- Data Science Math Skills – Duke University
- Introduction to Calculus – The University of Sydney
- Mathematics for Data Science Specialization – HSE University
- Probability and Statistics – University of London
Data Wrangling Courses:
- Data Wrangling with MongoDB – Udacity
- SQL Essentials Training and Certification – Edureka
- Data Wrangling Analysis and AB Testing with SQL – University of California
- Excel to MySQL Analytic Techniques for Business Specialization – Duke University
- Learn SQL basics for Data Science Specialization – University of California
- Databases and SQL for Data Science with Python – IBM
- Modern Big Data Analysis with SQL Specialization – Cloudera
- Introduction to Structured Query Language – University of Michigan
- Data Warehousing for Business intelligence Specialization – University of Colorado
- The Complete SQL Bootcamp 2022 – Udemy
- SQL – MySQL for Data Analytics and Business Analysis – Udemy
Data Visualization Courses:
- Data Analysis and Visualization with Power BI – Udacity
- Data Visualization – Udacity
- Tableau Certification Training – Edureka
- Data visualization with Tableau Specialization – University of California, Davis
- Data Visualization with Advanced Excel – PWC
- Information Visualization Specialization – New York University
- Information Visualization Advanced Techniques – New York University
- Data Visualization with Python – IBM
- Data Visualization and Communication with Tableau – Duke University
- Tableau 2020 Certified Associate Exam Guide A-Z (w Datasets) – Udemy
- Complete tableau 2021 training for Absolute Beginners – Udemy
Books:
Programming Language Books:
- Python Crash course – Eric Matthes View on Amazon
- Head first Python: A Brain Friendly Guide – Paul Barry
- Learn Python the hard way – Zed. A Shaw
- Intro to Python for Computer Science and Data Science Learning to Program with AI, Big data and Cloud – Paul Deitel
- Automate the boring stuff with Python – Sweigart
- R for data science – Hadley Wickham
- Machine Learning with R – Brett Lantz
- The book of R: A first course in Programming and Statistics – Tilman M.Davies
Machine Learning Books:
- Hands on Machine Learning with Scikit – Learns, Keras and Tensorflow – Aurelien Geron
- The Hundred page Machine Learning book – Andriy Burkov
- Machine Learning for Absolute Beginners – Oliver Theobald
- Machine Learning: An applied Mathematics Introduction – Paul Wilmott
- Introduction to Machine Learning with Python: A Guide for Data Scientists – Andreas C.Muller
Statistics Books:
- An Introduction to Statistical Learning – Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- Practical Statistics for Data Scientists – Peter Bruce
- Probability and Statistics for Data Science – Norman Matloff
- The art of Statistics: How to learn from Data – David Spiegelhalter
- How to lie with Statistics – Darrell Huff
Mathematics Books:
- Introduction to Probability – Joseph K. Blitzstein, Jessica Hwang
- Mathematics for Machine Learning – Marc Peter Deisenroth
- Linear Algebra and Optimization for Machine Learning – Charu C.Aggarwal
Data Wrangling Books:
- The Data Wrangling workshop – Brian Lipp
- Hands on Data Analysis with Pandas efficiently perform data collection, wrangling, analysis and visualization using python – Stefanie Molin
- Data Analysis from Scratch with Python – Peters Morgan
- Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IPython – Wes Mckinney
Data Visualization Books:
- Storytelling with Data: A Data Visualization guide for business professionals – Cole nussbaumer knaflic
- Data VisualizationL: A practical introduction – Kieran Healy
- Effective Data Visualization: The right chart for the Right Data – Stephanie Evergreen
- Data Visualization Handbook for data driven design – Andy Kirk
Conclusion:
Be in Touch with our blog to get latest updates on Free Best Online Resources to learn Data Analysis like free courses from top universities and Companies with projects.
This is the complete list of the most essential and desired skills to become a data analyst. Along with that, we have also mentioned some of the best online resources to learn data analysis based on the skills given. These come with live projects and training affiliated with top universities and companies that boost your portfolio.
There might be several other online resources too that may help you to learn about data analysis easily based on your level of understanding. Do connect with us and let us know if you know any more resources online through our social media sites.
