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Data Science

Data

Science

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights.

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data and apply knowledge from data across a broad range of application domains. Data science is related to data mining, machine learning, big data, computational statistics and analytics.
Data Science
Timeline and Prerequisite
6 - 7 Months
Course Duration
Prerequsite
Any graduate, Statistical analysis and programming will help you master the skills needed to become a Data Scientist.
Data Science
Curriculum
  • Introduction to Data Science
  • Mathematical & Statistical Skills
  • Machine Learning
  • Coding
  • Algorithms used in Machine Learning
  • Statistical Foundations for Data Science
  • Data Structures & Algorithms
  • Scientific Computing
  • Optimization Techniques
  • Data Visualization
  • Matrix Computations
  • Scholastic Models
  • Experimentation, Evaluation and Project Deployment Tools
  • Predictive Analytics and Segmentation using Clustering
  • Applied Mathematics and Informatics
  • Exploratory Data Analysis
  • Business Acumen & Artificial Intelligence
  • ....
Data Science
Modules
  • Module 1: Python

    Python is the most important and necessary topic that every data scientist should have knowledge about. In this section, our instructors will take you through the basics of Python and areas where it can be used. You will learn how to use some of the current tools such as Numpy, Pandas, and Matplotlib. Module 1 includes :

    • Environment set-up
      • Jupyter overview
      • Python Numpy
      • Python Pandas
      • Python Matplotlib
  • Module 2: R

    Used for statistical and data analysis, R programming language is one of the advanced statistical languages used in data science. This module teaches you how to explore data sets using R. Here you will learn :

    • An introduction to R
      • Data structures in R
      • Data visualization with R
      • Data analysis with R
  • Module 3: Statistics

    When working with data, the knowledge of statistics is necessary and an important skill set that you must have. In this module, you will learn :

    • Important statistical concepts used in data science
    • Difference between population and sample
    • Types of variables
    • Measures of central tendency
    • Measures of variability
    • Coefficient of variance
    • Skewness and Kurtosis
  • Module 4: Inferential statistics

    Inferential statistics is used to make generalizations of populations, from which samples are drawn. This is a new branch of statistics, which helps you learn to analyse representative samples of large data sets. In this module, you will learn :

    • Normal distribution
      • Test hypotheses
      • Central limit theorem
      • Confidence interval
      • T-test
      • Type I and II errors
      • Student’s T distribution
  • Module 5: Regression and Anova

    This lesson will help you understand how to establish a relationship between two or more objects. ANOVA or analysis of variance is used to analyse the differences among sample sets. Here you will learn :

    • Regression
    • ANOVA
    • R square
    • Correlation and causation
  • Module 6: Exploratory data analysis

    In this lesson you will learn :

    • Data visualisation
    • Missing value analysis
    • The correction matrix
    • Outlier detection analysis
  • Module 7: Supervised machine learning

    This is a comprehensive module to help you understand how to make machines or computers interpret human language.

    • Python Scikit tool
    • Neural networks
    • Support vector machine
    • Logistic and linear regression
    • Decision tree classifier
  • Module 8: Tableau

    Tableau is a sophisticated business intelligence tool used for data visualisation.

    • Working with Tableau
    • Deep diving with data and connection
    • Creating charts
    • Mapping data in Tableau
    • Dashboards and stories
  • Module 9: Machine learning on cloud
    • ML on cloud platform
    • ML on AWS
    • ML on Microsoft Azure