Data Analytics

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About Course

Data Analytics is the process of applying statistical analysis and logical techniques to extract information from data. When carried out carefully and systematically, the results of data analysis can be an invaluable complement to qualitative research in producing actionable insights for decision-making.

If that sounds a lot like data science, you’re right! It’s a closely related field, but there are important differences. Data scientists typically come from computer science and programming backgrounds and rely on coding skills to build algorithms and analytic models to automate the processing of data at scale. Data analysts typically have backgrounds in mathematics and statistics, and frequently apply these analytic techniques to answer specific business problems – for example, a financial analyst at an investment bank.

what skill do I need to become a Data Analytics?

Data analysts don’t do as much coding as data scientists, but it’s still important to know your way around certain programming languages. In particular, SQL (Structured Query Language) is the industry standard for navigating large databases, and statistical programming languages like R or Python are essential for performing advanced analyses on this data.

Data Analytics also relies on more typical business programs. While Microsoft Excel isn’t as powerful as SQL, R, and Python, it can get the job done when working with relatively smaller datasets and maybe the best (and cheapest) tool for the job for early-stage lean startups. Data visualization and presentation skills are also a huge part of the job, which typically requires learning new programs like Tableau as well as mastery of standard business software like Excel and Powerpoint.

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What Will You Learn?

  • -Understand what a relational database is
  • -Design and handle data using SAS and SQL
  • -Frame questions to generate data-based insight: Identify specific objectives and related hypotheses to drive data analysis. Avoid biases in interpreting data: Sidestep the common pitfall of unconsciously bending data to support false assumptions and preconceptions.

Course Content

Data Analytics

  • Introduction
    00:00
  • Course Overview
    00:00
  • Course Overview (cont’d)
    00:00
  • Descriptive Statistics – Graphical Approaches
    00:00
  • Descriptive Statistics – Measures of Central Tendency
    00:00
  • Descriptive Statistics – Measures of Dispersion
    00:00
  • Draft Lesson
  • Random Variables and Probability Distributions
    00:00
  • Probability Distributions(contd)
    00:00
  • Probability Distributions(contd)
    00:00
  • Inferential Statistics – Motivation
    00:00
  • Inferential Statistics – Single sample tests
    00:00
  • Two Sample tests
    00:00
  • Type 1 and Type 2 Errors
    00:00
  • Confidence Intervals
    00:00
  • ANOVA and Test of Independence
    00:00
  • Short Introduction to Regression
    00:00
  • Introduction to Machine Learning
    00:00
  • Supervised Learning
    00:00
  • Unsupervised Learning
    00:00
  • Simple and Multiple Regression in Excel and Matlab
    00:00
  • Regularization/ Coefficients Shrinkage
    00:00
  • Data Modelling and Algorithmic Modelling Approaches
    00:00
  • Ordinary Least Squares Regression
    00:00
  • NPTEL MOOC IDA – Tutorial for Assignment 2
    00:00
  • Training a Logistic Regression Classifier
    00:00
  • Logistic Regression
    00:00
  • Classification and Regression Trees(contd)
    00:00
  • Classification and Regression Trees
    00:00
  • Bias Variance Dichotomy
    00:00
  • Support Vector Machines
    00:00
  • Support Vector Machines(contd)
    00:00
  • Support Vector Machines for Non Linearly Separable Data
    00:00
  • Support Vector Machines and Kernel Transformations
    00:00
  • Model Assessment and Selection
    00:00
  • NPTEL MOOC IDA Tutorial for Assignment 3
    00:00
  • Deep Learning
    00:00
  • Artificial Neural Networks
    00:00
  • Ensemble Methods and Random Forests
    00:00
  • Artificial Neural Networks(cont\’d)
    00:00
  • Associative Rule Mining
    00:00
  • Association Rule Mining (contd)
    00:00
  • Big Data, A small introduction
    00:00
  • Clustering Analysis
    00:00
  • Big Data – A small introduction (contd)
    00:00
  • An Introduction to Online Learning – Reinforcement Learning
    00:00
  • An Introduction to Online Learning – Reinforcement Learning (contd)
    00:00
  • Clustering Analysis (contd)
    00:00
  • Introduction to Experimentation and Active Learning(contd)
    00:00
  • Introduction to Experimentation and Active Learning
    00:00

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