Data Analytics Techniques New Delhi

IIM Skills provides in-depth training on various data analytics techniques, designed to equip learners with the skills needed to extract meaningful insights from data. Here’s a detailed look at some of the key techniques covered:

1. Descriptive Statistics

Descriptive statistics involve summarizing and describing the basic features of data. Techniques include:

• Measures of Central Tendency: Mean, median, and mode.


• Measures of Dispersion: Range, variance, and standard deviation.


• Data Distribution: Histograms and frequency distributions.


• Data Summarization: Creating summary tables and descriptive summaries to understand data patterns and trends.

2. Data Visualization

Data visualization techniques help present data in a graphical format, making it easier to spot trends and patterns. Key methods include:

• Charts and Graphs: Bar charts, line graphs, pie charts, scatter plots.


• Dashboards: Interactive visual interfaces that combine multiple visualizations to provide an overview.


• Tools: Software such as Tableau, Power BI, and tools within Python like Matplotlib and Seaborn.

3. Exploratory Data Analysis (EDA)

EDA is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. It includes:

• Data Profiling: Understanding the data’s structure, missing values, and types.


• Univariate Analysis: Analyzing individual variables using histograms, box plots, and summary statistics.


• Bivariate and Multivariate Analysis: Exploring relationships between two or more variables using scatter plots, correlation matrices, and pair plots.

4. Predictive Analytics

Predictive analytics involves using historical data to forecast future events. Techniques include:

• Regression Analysis: Linear and logistic regression models to predict continuous and categorical outcomes.


• Time Series Analysis: Methods like ARIMA (AutoRegressive Integrated Moving Average) for forecasting trends over time.


• Classification: Techniques such as decision trees, random forests, and support vector machines (SVM) for categorizing data into classes.

5. Inferential Statistics

Inferential statistics help make predictions or inferences about a population based on sample data. Techniques include:

• Hypothesis Testing: Testing assumptions using t-tests, chi-square tests, and p-values.


• Confidence Intervals: Estimating the range within which a population parameter is likely to fall.


• ANOVA (Analysis of Variance): Comparing means across multiple groups to determine if they are significantly different.

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