Data Science

Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. At the core is data. Troves of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities can be build with it. Data science is ultimately about using data in creative ways to generate business value:

This aspect of data science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences. It's about surfacing hidden insight that can help enable companies to make smarter business decisions

Internship Batch Date:

6th Jun 2019 13th Jun 2019 20th Jun 2019
27th Jun 2019 4th Jul 2019 18th Jul 2019

Information:

Registration Amount 12999 /- INR
Internship Type Virtual / Classroom Program

Curriculum

  • What is DataScience
  • Install Python and Anaconda.
  • Installing packages: numpy, pandas, matplotlib, sklearn)
  • Introduction to Python
  • Flow Control (If, for, while) Statements
  • Data Structures
    • Numbers
    • Lists
    • Tuples
    • Dictionary
    • Strings
  • Functions and classes in Python
  • Ndarray Object
  • Data Types in Numpy
  • Array Attributes and Manipulation in Numpy
  • Indexing & Slicing
  • Iterating Over Array
  • Binary Operators in Numpy
  • Mathematical Functions in Numpy
  • Arithmetic Operations in Numpy
  • Matrix Library in Numpy
  • Data Structures in Pandas
  • Series and DataFrame in Pandas
  • Basic Functions in Pandas
  • Iteration
  • Sorting
  • Reindexing
  • Indexing and Selecting Data
  • Missing Data
  • Groupby
  • Merging/ Joining
  • Concatination
  • Categorical Data
  • 2D Plotting with matplotlib
  • Plotting with keyword strings
  • Plotting with categorical variables
  • Regression
    • Simple Linear Regression
    • Multiple Linear Regression
    • Support Vector Regression
    • Decision Tree Regression
    • Random Forest Regression
  • Classification
    • Support Vector Classification(SVM)
    • K - Nearest Neighbour Algorithm(KNN)
    • Naive Bayes Classification
    • Decision Tree Classification
    • Random forest Classification
  • K-means Clustering Algorithm
  • Hierarchical clustering