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Data Science

Learn by working on an end to end Data Science project by Industry Expert.

What you'll learn
  • Successfully perform all steps in a complex Data Science project
  • Create Basic Tableau Visualisations
  • Understand how to apply the Chi-Squared statistical test
  • Apply Ordinary Least Squares method to Create Linear Regressions
  • Assess R-Squared for all types of models
  • Create a Multiple Linear Regression (MLR)
  • Apply SQL to Data Science projects
data science

Data Science

Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!

Course Content

Section A: Introduction to Data Science
  • What is data Analysis?
  • What is Python?
  • Why python for data Analysis?
  • Essential Python Libraries
  • Installation and setup
  • Jupiter Notebook
  • PY-Charm
Section B: Numpy Arrays
  • Creating multidimensional array
  • NumPy Data types
  • Array attributes
  • Indexing and Slicing
  • Creating array views and copies
  • Manipulating array shapes
Section C: Statistics and Linear Algebra
  • Basic statistics with Numpy
  • Linear Algebra with Numpy
  • Numpy random numbers
  • Creating a Numpy masked array
Section D: Intermediate Python for Data Science
  • Matplotlib
  • Dictionaries & Pandas
  • Logic, Control Flow and Filtering
  • Loops
  • Case Study
Section E: Importing Data in Python
  • Introduction and flat files
  • Importing data from other file types
  • Working with relational databases in Python
  • Importing data from the internet
  • Interacting with APIs to import data from the web
  • Dividing deep into the Twitter API
Section F: Working With Pandas
  • Installing pandas
  • Pandas data frames
  • Pandas Series
  • Data aggregation with Pandas Data Frames
  • Concatenating and appending Data Frames
  • Joining Data Frames
  • Handling missing data
  • Time series in pandas
Section G: Manipulating Data Frames with Pandas
  • Extracting and transforming data
  • Advanced indexing
  • Rearranging and reshaping data
  • Grouping data
  • Bringing it all together
  • Preparing data
  • Concatenating data
  • Merging data
  • Case Study - Summer Olympics
Section H: Analyzing Police Activity with Pandas
  • Preparing the data for analysis
  • Exploring the relationship between gender and policing
  • Visual exploratory data analysis
  • Analyzing the effect of weather on policing
Section I: Intro to SQL for Data Science
  • Selecting columns
  • Filtering rows
  • Aggregate Functions
  • Sorting, grouping and join
Section J: Introduction to Shell for Data Science
  • Manipulating files and directories
  • Manipulating data
  • Combining tools
  • Batch processing
  • Face Recognition
  • Case Study - Summer Olympics
  • Twitter Sentiment Analysis
  • Smart Attendance system
  • Recommender systems

Coming Soon...


  1. 1. Basic Knowledge of Python.
  2. 2. Should have laptop.

Course Outcomes!!

  1. 1. Writing tip to showcase skills you have learnt in the course.
  2. 2. Mock interview practice and frequently asked interview questions.
  3. 3. Career guidance regarding hiring companies and open positions.
  4. 4. Certificates
  5. 5. Opportunity of Research Internship