Data analytics goal is to list patterns and trends hidden deep into the data by leveraging mathematical models and statistical techniques to infer and predict. It is used to draw conclusions about the information they contain. A statistical model work with several independentand dependent variables.

Training Methodology Steps

  • Modes

    Defined in two modes

  • Experiment Learning

    Comprehensive hands on program giving you the utmost confidence you need to be better competent.

  • Reinforcement

    Periodic evaluation and assessment will help you stay aligned and forward.

  • Technology Compliant

    Our Learning Management System will help you access resources you need in pursuit of your learnings.

Course Curriculum

Week 1 - 8hrs

- An introduction to data analysis.
- Brush up mathematical & statistical concepts.
- Key differences with analysis and analytics.
- Who uses statistics anyhow.?
- An approach to understand statistics.
- Sub-divisions in statistics.
- A case study to understand usage of analytics.
- Dealing and arranging the data.
- What are data arrays and frequency distribution.
- Constructing a frequency distribution.
- Measures of Central tendency & dispersion.
- Descriptive statistics - mean | median | mode | std. dev.

Week 2 - 8hrs

- Data Exploration.
- Load different format of data into the software (CSV, TEXT, SQL).
- Plotting data.
- Generating frequency distributions.
- Sample data set / Remove duplicate values of a variable.
- Treat missing values and outliers.
- Merge / join datasets.
- Work with different types of visualizations.
- Interpret the descriptive data from the visualizations.
- Work with both summarized and statistical charts.
- Explore different charting libraries in the given tool.
- Correlation analysis, find the right candidate variable for regression.
- Understand various forms of data distributions (Normal, BiNormal).

Week 3 - 8hrs

- Introductory idea in Probability.
- Study of odds & Ends.
- Types of probabilities in analytics.
- Probability independence and dependence.
- Probability - Bayes theorem.
- Probability distributions.
- Binomial/Poisson/Normal/Continuous distributions.
- Hypotheses testing procedure.
- Chi-square & ANOVA.
- Simple/Multiple Regression & Correlation.
- Making inferences about population.
- Tests of significance in statistics.
- Time series & forecasting.

Week 4 - 8hrs

- What is machine learning.?
- Practical applications to machine learning.
- Types of machine learning (supervised & unsupervised).
- Generalized linear models.
- Classification: Logistic regression.
- Classification: Decision trees & random forest.
- Classification: Support vector machines.
- Cluster analysis: calculating distances.
- Cluster analysis: Hierarchical cluster analysis.

Technologies Used

Our Approach

Practical Coverage

Deal with diversed data sources to consolidate into data source keeping all business parameters in place.

Logical workflows meeting or seeing specific business problems.

Learn data workflow automation

Learn to deploy workflow jobs on the cloud.

Delivery Methods


Learn the technology backed with comprehensive Learning Management System.


Access to more interactive sessions.

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