Basic concepts in python, Calculations in python, Variable assignment, Function, Conditions, Data structures - List, Dictionaries, Numpy array, Slicing, Splicing, Subsetting, Functions, Conditions, Loops, Keys, Values, Datatypes.
Statistics / Plotting - Seaborn vs Matplotlib, Univariate analysis - Import from csv, Plot histograms, Distribution, Mean, Data with same mean but different standard deviation, Data with same mean and standard deviation but different kurtosis, Bootstrapping and subsetting - making samples, Mean of sample, Central limit theorem, Plotting, Hypothesis testing, Bivariate analysis- correlation, Scatter plots, Stratified samples, Categorical , Class variable.
Series - Datatypes, Index, Data frame - series to data frame, Reindex, Grouping, Pandas shortcuts, Reading from different sources, Missing data treatment, Merge, Join, Writing to file, Database operations.
Regression- Data Aggregation, Filtering, Lamda functions, Map, Filter, Visualization, Matplotlib, Pyplot, Scatterplot, Histogram, Heatmaps.
Regression – Linear, Lasso, Ridge, Variable selection, Forward & Backward regression, Polynomial regression.
Logistics regression, Naïve Bayes.
Unsupervised learning, Distance concepts, Classification, k-nearest, Clustering, k-means, Multidimensional scaling.
Decision Trees, Random Forest, Boosted Trees, Gradient Boosting.
Who Should Attend?
After completing this course and successfully passing the certification examination, the student will be awarded the “Python Programming” certification.
If a learner chooses not to take up the examination, they will still get a 'Participation Certificate'