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
  • 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

Basic Statistics

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

Using Data Science Libraries

  • Numpy
  • Pandas
  • Matplotlib
  • Scipy
  • Scikit-learn
  • Others

Data Science Work Flow (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 Work Flow

  • Choosing features
  • Selecting Model
  • Supervised vs. Unsupervised learning
  • Using different algorithms like Logistic Regression, Naive Bayes, Decision Tree etc. using Scikit-learn
  • Training Model
  • Evaluating result of the model using metrics
  • Presenting the model - Deployment

Working with Regression case study