Data analytics refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning.
There are several applications of business analytics today. It is used across industries and job functions. It is widely used in Health care, Banking, IT, Insurance and basically any industry that generates and uses data to make decisions. Some applications of business analytics include
- Marketing analytics
- Pricing analytics
- Risk & Credit analytics
- Fraud analytics
- Health care analytics
- Financial Services analytics
- Social media data analytics and more
This course has been designed keeping in mind that some of the candidates who wish to do data analysis don't come from a technical or engineering background. The focus has been kept on statistics and its use for data collating and analysis.
- 40 hours of online instuctor led training or classroom training
- Taught by Industry experts
- Learn In-demand skills and the software used in the industry
- Real world case studies
- Placement assistance
This course will make you comfortable with reviewing and working with data which is the basis of all analysis and decisions we take today. Whether you are working in Finance, marketing, HR, IT or consulting, this course will help you take big strides in your career.
Online instructor led sessions and Classroom training
Duration: 10 weeks
Duration of class: 2 hours
Days: Saturdays & Sundays
INR 32,500 inclusive of taxes
1. Introduction to Statistics and Data Analytics - Sample Vs. Population, Variables and Types of Data, Primary & Secondary Data, Data Collection and Sampling Techniques
1. Descriptive Statistics - Measure of Central Tendency - Mean, Median, Mode, Measure of Variance - Range, Inter Quartile Range, Variance & Standard Deviation, Coefficient of Variation, Dispersion, Kurtosis, Skewness, Chebyshev's Theorem, Measures of Positions - Percentile, Deciles, Quartiles.
2. Introduction to Random Variables (Discrete and Continuous Random Variables), Exploratory Data Analysis, Frequency Tables and Frequency Distributions, Type of Graphs
3. Inferential Statistics & Hypothesis Testing - Formulation of Hypothesis Statement, p-value, Type I and Type II Errors, Z-Test, t-Test, Chi-Square Test
4. Introduction to Statistical Estimation and Confidence Interval
5. Probability Theory - Introduction to Probability Theory & Counting Rules
6. Probability Distributions - Discrete and Cumulative Probability Distribution, Sampling Distribution, Binomial Distribution, Standard Normal Distribution, Poisson Distribution.
7. Chi-Square, F- Distribution, and ANOVA (One - Way and Two-Way ANOVA)
8. Correlation and OLS/Multiple Regression (Logistic and Linear Regression)
1. Statistical Analysis using Excel
2. Introduction to R software, and Statistical Analysis using R
3. Introduction to Tableau & its application in Analytics
4. Introduction to Python and its application in Analytics
* Data Analyst
* Business Analyst
* Business Intelligence Analyst
* Analytics Consultant
* Data Analyst