SASA conducts the following capacity building programmes on Data Analytics for upskilling the employees of the Department of Economics & Statistics.

Data Analysis using R

Data analysis in R has become a cornerstone of modern statistical computing and data science due to its flexibility, extensibility, and strong community support. R is an open-source programming language specifically designed for statistical analysis, data visualization, and reporting.

R provides a comprehensive environment for handling the entire data analysis workflow—from data import and cleaning to modeling and visualization. With built-in functions and a vast ecosystem of packages, analysts can efficiently perform exploratory data analysis (EDA), apply statistical tests, and build predictive models. R also supports various statistical techniques, including regression analysis, time series analysis, classification, clustering, and machine learning.

Course Syllabus of Data Analysis in R

Introduction to R programming, installation on R and R- Studio in Windows, Linux or Mac, data types in R, functions in R, vectors, metrices and dataframes, Data visualisation, data manipulation, data visualisation, inferential statistics and hypothesis testing, regression anlysis, time series handling in R and forecasting 

Data Analytics using Python

Data analysis in Python has emerged as a powerful and widely adopted approach in modern data science due to its simplicity, versatility, and extensive library ecosystem. Python is an open-source programming language that supports efficient data handling, statistical analysis, and visualization, making it suitable for both beginners and advanced users.

Python offers a robust set of libraries for data analysis. Libraries such as Pandas enable efficient data manipulation and cleaning, while NumPy provides support for numerical computations and array operations. For data visualization, tools like Matplotlib and Seaborn allow users to create informative and visually appealing charts and graphs. Additionally, SciPy and scikit-learn facilitate advanced statistical analysis and machine learning applications.

A key advantage of Python is its ability to handle large datasets and integrate seamlessly with databases, web applications, and other programming languages. It supports the entire data analysis workflow, including data collection, preprocessing, exploration, modeling, and interpretation. Python also promotes reproducibility and collaboration through tools such as Jupyter Notebooks, which allow analysts to combine code, visualizations, and explanatory text in a single interactive document. This makes it particularly useful in research, training, and professional reporting environments.

Course Syllabus of Python Data Aanalytics

Introduction to Python, data types, variables, datasets, file handling, functions, libraries, introction to Python IDE like Google Colab, Spyder, Jupiter etc, exploratory data analysis, data visualisation methods, regression analysis, time series data handling, forecasting, inferential statistics, hypothesis testing

Data Analysis using GIS

Geospatial data analysis is an indispensable component of modern data-driven decision-making. By providing spatial insights and visual context, it enhances the ability of organizations and governments to plan efficiently, respond effectively, and achieve sustainable development outcomes. Geospatial Data Analysis refers to the process of collecting, processing, analyzing, and visualizing data that is associated with specific geographic locations. It integrates spatial (location-based) data with attribute (descriptive) data to uncover patterns, relationships, and trends that support informed decision-making.

Geospatial data can broadly be classified into two types: vector data (points, lines, and polygons representing discrete features such as roads, boundaries, and landmarks) and raster data (grid-based data such as satellite imagery and elevation models). The integration of these data types allows for comprehensive spatial analysis.

Key analytical techniques in geospatial data analysis include spatial querying, overlay analysis, buffer analysis, network analysis, and spatial statistics. These methods are used to address complex real-world problems by identifying spatial relationships such as proximity, distribution, clustering, and spatial correlation.

Course syllabus of GIS Data Analytics

Introduction to Geospatial Technology, Installation of QGIS open source software, concepts of vector and raster data, shapefiles, creating point, line and polygon  files, importing statistical data into QGIS, integration of data file with shapefile, spatial queries, georeferencing, digitalising the georeferenced data, GIS data visulisation using maps.