Srikanth Technologies

Data Science with Python

This course teaches how to use Python for Data Science and Machine Learning. It takes you through the life cycle of Data Science project using tools and libraries in Python.

Prerequisite Python Language
Theory Fee (Classroom) Rs. 5000/- (Includes digital course material)
Theory Fee (Online) Rs. 5000/- (Includes digital course material)
Lab Fee for Classroom Students Rs. 1000/-
Digital or Physical Certificate Fee Rs. 200/-
Software Required

Introduction To Data Science

  • What is Data Science
  • What is Machine Learning
  • What is Deep Learning
  • Role of Data Scientist
  • Applications of Data Science
  • Data and its sources
  • Overview of Data Science Life Cycle

Working with Anaconda and Jupyter Notebook

  • Downloading and installing Anaconda
  • Starting Jupyter Notebook
  • UI elements of Notebook
  • Kernel and types of cells - Code and Markdown
  • Modes - Edit and Command
  • Magic functions - Line and Cell functions
  • Keyboard shortcuts - Command mode and Edit mode shortcuts
  • Saving and loading of notebook
  • Working with
  • Using Jupyter Lab

Basic Statistics

  • Mean, Median, Mode and Range
  • Variance and Standard Deviation
  • Quartiles and IQR
  • Scatter Plot, Bar Graph, Histogram, Pie, Box plot
  • Measuring Skewness
  • Probability
  • Regression Analysis
  • Using statistics and scipy.stats libraries to apply Linear Regression


  • Creating single and multi-dimensional arrays
  • Using fancy indexing and slicing
  • Array operations, methods of ndarray and universal functions
  • View vs. Copy of array
  • Reshaping arrays
  • Stacking and splitting arrays
  • Broadcasting
  • Applying Linear Algebra
  • Image processing with Arrays


  • Working with Series
  • Applying methods on Series
  • Working with DataFrame
  • Reading data into DataFrame and writing DataFrame to other formats
  • Selecting rows and columns in DataFrame
  • Adding and deleting rows and columns in DataFrame
  • Working with apply() and applymap() functions
  • Working with str attribute for string manipulations
  • Joining, Merging and Concatenating DataFrames
  • Grouping data on one or more columns
  • Data Wrangling - Binning, Encoding etc.
  • Handling null values


  • Anatomy of a figure
  • Working with Module API and Object API
  • Working with different plots - Histogram, Bar, Stacked Bar, Pie, Scatter, Line
  • Creating multiple axes in single figure
  • Customizing plots


  • Figure-level vs. axes level plots
  • Categorical, Relational, Distribution, Regression and Matrix Plots
  • Using parameters like hue, row and col

Data Science Workflow (Life Cycle) using Classification Case Study

  • What is the question
  • Data Acquisition
  • Preparing data - cleaning and organizing data
  • Exploratory Data Analysis (EDA)
  • Data Munging/Data Wrangling
  • Feature Engineering
  • Data Visualization

Machine Learning Workflow with Classification Case Study

  • Understanding pre-processing concepts like Standardization, Encoding etc.
  • Understanding Regularization - Lasso and Ridge
  • Using different algorithms like Logistic Regression, Naive Bayes, Decision Tree etc. using Scikit-learn
  • Understanding Gradient Decent and XGBoost
  • Training Model
  • Evaluating result of the model using metrics - classification report, confusion matrix
  • Understanding cross validation and how to use it
  • Using Grid Search to select right hyper parameters
  • Presenting the model - Deployment

Working with Regression case study

Unsupervised Machine Learning

  • What is clustering
  • How k-Means clustering works
  • How hierarchical clustering works
  • Recommender systems - Collaborative filtering and Content-based filtering