List of data science online courses for free 2016 & 2017

Data science online course for beginners and experienced:

There are lots of data science tutorial are available for the beginners to advanced level. Also, we have to learn about Python and R programming tutorials for using the statistical calculations. We are recommended to study the following courses which are available free of cost.

For references:

List of companies hiring the data scientist

Data science interview questions for data scientist/data analyst (free download)

Machine Learning:

The following resources are useful for the beginners to learn about the basic machine learning techniques.

  1. Introduction to machine learning- It’s the basic thing to learn about what is machine learning, the basic concepts of data mining, applications, classifications and regressions.
  2. Application of the machine learning- You have to know about the application and the issues in the process.
  3. Learn the advantages of classification algorithms- There are different classifier algorithms are available such as Support Vector Machines (SVMs), Logistic Regression, Tree Ensembles, Boosting, etc., Based on the size of training datasets and features you can select the best algorithm.
  4. Clustering analysis and dimension reduction- Learn the detailed description of the Principal Component Analysis (PCA), Random projections and Applying the PCA.
  5. Anomaly detection machine learning- Calculate the mean and standard deviations of the data sets, those SD values goes outside the range of the mean is called anomalous value.

Machine learning algorithm for classifications


This part makes you to learn more about the data analysis and give more information on “how to handle the datasets?”

  1. Statistical modelling- It is the foundation for learning the statistical interfaces. This is the basics for statistical hypothesis tests and statistical estimators. This model is specified by the mathematical equations.
  2. Theories and Complexities- Theories such as Bayesian, frequentists, design based, and many more can be learned to know the complexities in formation of statistical interface.
  3. Statistical interface- It is useful for analysis of datasets.

Python Programming:

Learning programming is much important for the machine learning and data analysis.

  1. Introduction to Python and Ipython- Learn about interactive programming for computing and the basics as well as advanced Ipython features
  2. Numpy basics and pandas- How to use the NumPy, arrays, Data processing, file input and file output using arrays, Learn about the pandas and computing descriptive statistics.
  3. Machine learning with scikit-learn
  4. Plotting and Visualization- Go through the plotting functions in the pandas and real world examples like plotting the visualization of earthquakes data’s.

Python interview questions in PDF (Free Download)

R Programming:

R programming is very useful for statistical calculations and graphics.

  1. Introduction to R- Go through the basics in the R language.
  2. Application of R- It is widely used in statistical oriented companies, data mining companies and much more.
  3. Time series analysis
  4. Programming features in R programming language.

Download R interview questions Free in PDF

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