Data Science with Python

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Python has a very simple grammar and hence easier for everyone to pick up the language quickly. It is an all-round language for web, desktop, embedded programming for applications in machine learning, deep learning, IoT etc. It's an added advantage for professionals to learn Python along with R

This course introduces the students with fundamentals and advanced level of Python programming, installing Anaconda, programming basics, data types, list, tuples, dictionary, controls, loops, Numpy, Pandas, functions, importing & scraping data, and visualization. Once, students are conversant with Python, a detailed study of data science which includes data mining & machine learning, starts. Machine Learning covers linear & generalized linear models, KNN, Naïve Bayes, Tree based models, SVM, K-means, Association rule, performance measures, dimension reduction techniques, randomization, cross validation, bootstrapping, ROC & AUC, and confusion matrix.

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DESCRIPTION

Python has a very simple grammar and hence easier for everyone to pick up the language quickly. It is an all-round language for web, desktop, embedded programming for applications in machine learning, deep learning, IoT etc. It's an added advantage for professionals to learn Python along with R

This course introduces the students with fundamentals and advanced level of Python programming, installing Anaconda, programming basics, data types, list, tuples, dictionary, controls, loops, Numpy, Pandas, functions, importing & scraping data, and visualization. Once, students are conversant with Python, a detailed study of data science which includes data mining & machine learning, starts. Machine Learning covers linear & generalized linear models, KNN, Naïve Bayes, Tree based models, SVM, K-means, Association rule, performance measures, dimension reduction techniques, randomization, cross validation, bootstrapping, ROC & AUC, and confusion matrix.  

This course prepares on the practical applications of Machine Learning algorithms & techniques using Python. 


  • Installing Anaconda, installing libraries using “pip” and “conda”, programming basics, data types, list, tuples, dictionary, sets, numpy & pandas, slicing & dicing, controls, loops, and functions 
  • Packages:

                + Data pre-processing – detection & correction of missing data (imputation), outliers, errors etc 

                + Importing / Web scraping .

                + Manipulating data .

                + Visualization using matplotlib, seaborn, bokeh, bgplot, plotly, ggpy, altair, chaco.

                + Modelling data using scikit-learn package .

                + Report & result using ipython .

                + Project using Python and Machine Learning concepts

  • Basic knowledge of Linear Algebra, Calculus, Statistics, and Probability 
  • Knowledge of basics of programming

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