Datails - StepUp Analytics

Business Analytics Using Python Programming



Workshop Start Date: 01-02-2020

Workshop End Date: 23-02-2020

Venue: Online

Registration Ends: 31-01-2020

Fee: Rs.650

Duration: 16 Hours (4 Weekends)


Introduction To Python Programming

  • Introduction to Anaconda and iPython Notebook
  • Custom Environment Settings
  • Python Basic Rules in Python 3
  • Concept of Packages/Libraries (NumPy, SciPy, Pandas, Matplotlib, etc.)
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels –  Date & Time Values
  • Basic Operations - Mathematical - string - date
  • Reading and writing data
  • Simple plotting/Control flow/Debugging/Code profiling

Introduction to Business Analytics

  • What is Business Analytics?
  • Business Analytics vs. Data Analyst Vs Consultant, OLAP, MIS Reporting
  • Relevance in industry and need of the hour
  • Type of problems and objectives in various industries
  • How leading companies are harnessing the power of Analytics?
  • Different phases of a typical Analytics projects

Advanced Python Programming For Business Analytics

  • Cleansing Data with Python
  • Data Manipulation steps(Sorting, filtering, duplicates, merging, derived variables, etc..)
  • Data manipulation tools(Operators, Functions, control structures, Loops, arrays, etc.)
  • Python Built-in Functions (Text, numeric, date, utility functions) 
  • User-Defined Functions in Python 
  • Stripping out extraneous information
  • Normalizing data and Formatting data
  • Important Python Packages for data manipulation(Pandas, Numpy, etc.)

Accessing/Importing and Exporting Data

  • Overview of Python- Starting Python
  • Importing Data from various sources (CSV, txt, excel, access, etc.)
  • Database Input (Connecting to the database)
  • Viewing Data objects -  subsetting, methods
  • Exporting Data to various  formats

Data Visualisation Using Python

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