Databases and SQL for Data Science– IBMĥ. Learn SQL Basics for Data Science Specialization– University of California, DavisĤ. Excel to MySQL: Analytic Techniques for Business Specialization– Duke Universityģ. Probability and Statistics– University of LondonĢ. Introduction to Calculus– The University of Sydneyĩ. Probabilistic Graphical Models Specialization– Stanford UniversityĨ. Basic Statistics– University of Amsterdamħ. Probability – The Science of Uncertainty and Data– MITxĦ. Data Science Math Skills– Duke Universityĥ. Mathematics for Data Science Specialization– Courseraģ. Mathematics for Machine Learning Specialization– Imperial College LondonĢ. Statistics for Data Science and Business Analysis– Udemyġ. Business Statistics and Analysis Specialization– Rice Universityġ0. Statistical Analysis with R for Public Health Specialization– Imperial College Londonĩ. Basic Statistics– University of AmsterdamĨ. Data Science: Statistics and Machine Learning Specialization– Johns Hopkins Universityħ. Intro to Descriptive Statistics – (Free Course ) UdacityĦ. Intro to Inferential Statistics– (Free Course) Udacityĥ. Statistics with Python Specialization– University of MichiganĤ. Statistics with R Specialization– Duke Universityģ. Intro to Statistics– (Free Course) UdacityĢ. Programming for Data Science with R– Udacityġ. R Programming – Johns Hopkins Universityĩ. Python 3 Programming Specialization– University of MichiganĨ. Introduction to Python Programming– Udacityħ. Introduction To Python Programming– UdemyĦ. Python for Everybody– University of MichiganĢ. Applied Data Science with Python Specialization– University of Michiganġ. Data Analysis and Visualization– (Free Course) UdacityĨ. IBM Data Science Professional Certificate.– IBMħ. Data Analysis with R– (Free Course) UdacityĦ. SQL for Data Analysis– (Free Course) Udacityĥ. Data Science Specialization– Johns Hopkins UniversityĤ. Intro to Data Analysis– ( Free Course) Udacityģ. So let’s start with online courses- Online Courses TopicsĢ. Resources to learn Data Analysisįor your convenience, I have created separate tables for each resource. So, these are some must-have skills for Data Analysis, now let’s move to the Best Online Resources to Learn Data Analysis. All you need to know is its pros and cons, as well as when to and when not to apply these algorithms to a dataset. You don’t need to learn the theory and implementation details behind all ML algorithms. Not all Data Analysts have Machine Learning knowledge, but if you want to get the extra privilege, it’s better to have Machine Learning skills. Machine LearningĪfter having all previous skills, it’s good to have a basic knowledge of Machine Learning. By drag and drop, you can create a wonderful presentation report. You should know various Reporting tools like Tableau and power bi. These tools have in-built visualization reporting tools. And for that, you should be familiar with data visualization tools like ggplot, matplotlib, Seaborn, and D3.js. That’s why the knowledge of Data Visualization is important. This is an important step for a Data Analyst. Data visualizationĪs a Data Analyst, you have to showcase your findings in a visual form, so that stakeholders can understand them properly. You should also be familiar with relational databases such as PostgreSQL, MySQL, Netezza, and Oracle, as well as Hadoop, Spark, and MongoDB. So, for that, you should know about database systems- both SQL-based and NoSQL-based. Data Wranglingĭata wrangling is all about data collection and data cleaning. You should be familiar with multivariate calculus and linear algebra. Along with that, you should have an understanding of matrix manipulations, dot products, eigenvalues and eigenvectors, and multivariable derivatives. That’s why strong knowledge of Math is required. MathematicsĪs a data analyst, you have to deal with numbers. Statistics knowledge includes statistical tests, distributions, and maximum likelihood estimators. Statistics knowledge will give you the ability to decide which algorithm is good for a certain problem. To become a successful data analyst, you should know Statistics. Knowledge of all programming languages is not required. You must know one or more programming languages like Python, R, or SAS.Īlong with that, you should be familiar with data science libraries and packages (such as ggplot2, reshape2, NumPy, pandas, and scipy). This is the core skill that makes a Data analyst apart from Business Analyst. Programming knowledge is a must-have skill for a Data Analyst. Best Online Resources to Learn Data Analysisīefore discussing the resources, I would like to tell you what topics or skills you need to learn for data analysis- Skills Required for Data Analysis- 1. Programming
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