Data Analysis

• Understanding the numpy arrays

• Shapes, dimensions and reshaping

• Slicing the numpy arrays

• Understanding the fancy indexing, boolean indexing and vectorisation

• Numpy’s scientific libraries

Plotting & Visualisation

• Plotting and Visualisation libraries in python
  Matplotlib, pyecharts etc..

• Line plots, Pie charts, Scatter plots, Histograms

• Understanding Series and DataFrames

• Building a DataFrame from a csv file

• Getting the descriptive and summary statistics

• Joining and merging the dataframes

• Data Aggregation and group-by operations

• Handling the missing data

• Filtering and removing

• Handling the outliers

• Learning the Binning of data

• Applying the functions on the dataframe

• Understanding the joins – left join, right join

Pandas

The Basics Of Statistics

• Mean, median and standard deviation

• The z-scores

• Probability Distributions

• Understanding the Normal Distribution

• The standard Normal Distribution

Machine Learning

• Supervised and Unsepervised methods

• Installing and Learning the scikit-learn library

• Understanding the dataset

• Breaking the dataset into train and test

• Understanding stratified sampling

• Cleaning and finetuning the dataset

• Implementing the Linear Regression

• Understanding the loss function

• Making the prediction

Register Here