23 Mar, 2019 - 26 May, 2019

10:00 AM - 01:00 PM


  • 23 Mar, 2019 - 26 May, 2019
    10:00 AM - 01:00 PM


This course has been designed in two phases. Phase one introduces Python as a programming language and phase two surveys the foundational topics of data science such as data manipulation (using Numpy, Pandas), data communication and visualization (using Matplotlib, Seaborn), and data analysis with Statistics and Machine Learning (using Scikit-Learn).


Basic Knowledge of Statistics will be helpful

Faculty Profile

She is a technology and data enthusiast. She is currently working as a Data Scientist at S&P Global Market Intelligence, one of the leading providers of real-time data and analytics to institutional investors and corporations. She has over 3 years of experience in the field of Data Mining and Analytics. She likes to explore interdisciplinary Data Science to utilize technical skills borrowed from computer science and statistics to tackle real-world problems in social media, healthcare, and finance.
Professional Skills: Probability and Statistics, Linear Algebra, Machine Learning, Natural Language Processing, Deep Learning using Python



  • Overview of Python- Starting with Python
  • Introduction to installation of Python
  • Introduction to Python Editors & IDE's (Canopy, PyCharm, Jupyter, Rodeo, Ipython etc…)
  • Understanding Jupyter notebook & Customize Settings
  • Installing & loading Packages & Namespaces

Python Programming 1

  • Variables, operations, control flow - assignments, conditionals
  • Loops, functions
  • Python: types, expressions, strings, lists, tuples
  • Python memory model: names, mutable and immutable values
  • List operations: slices etc
  • Text, numeric, date, utility functions in Python

Python Programming 2

  • Dictionaries
  • More on Python functions: optional arguments, default values
  • Passing functions as arguments, Higher order functions on lists
  • Basic input/output, Handling files, File I/O, Reading & Writing data to Files
  • String processing, String slicing, Testing, searching and manipulating strings

Data Science using Python

  • NumPy – Usage, Examples and Application
  • Pandas – Usage, Examples and Application
  • Matplotlib – Usage, Examples and Application
  • Other Important Data Science Libraries in Python
  • NLP - Pattern Matching, Searching and Regular Expressions

Data Analysis – Visualization using python

  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
  • Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)

Data Exploration for Modelling

  • EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
  • Identify missing data
  • Identify outliers data

Introduction to Machine Learning

  • Common terminology used in analytics & modeling process
  • Popular modeling algorithms
  • Types of Business problems - Mapping of Techniques
  • Different Phases of Predictive Modeling
  • Supervised and Unsupervised Learning
  • Linear Regression in Python