eminent statistics

Internships of

Eminent Statistics

Unlock the Power of Data

Internship in R, Python & Power BI

Join our hands-on internship program crafted to help you master data analytics tools through real-time projects. Learn from experts, gain practical exposure, and boost your career with a professional certificate.

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Objective

To equip students and professionals with foundational and advanced skills in data analytics using industry-relevant tools.

Duration

75 Days (3 Months)

Program Focus

This internship is crafted to bridge academic learning with practical application. It’s ideal for those looking to enhance their real-world data handling, analysis, and visualization capabilities.

 
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Mode of Delivery

Both Online and In-Person formats are available for flexible learning.

Who Should Enroll ?

  • Students pursuing UG/PG in Statistics, Data Science, etc.

  • Fresh graduates seeking skill-building before job search

  • Professionals aiming to switch to data analytics

Internship Highlights

Key benefits that make this internship practical, flexible, and future-ready.

Live Projects

Gain hands-on experience with real-world data tasks.

Flexible Modes

Attend online or in-person sessions at your convenience.

Expert Mentorship

Learn directly from experienced statisticians and data scientists.

Verified Certification

Earn a certificate that validates your skills and effort.

Tool Training

Master tools like R, Python, Excel, and Power BI.

Career Advantage

Enhance your resume and job prospects with practical knowledge.

Mastery Map

What You Will Learn

Master practical skills and concepts essential for data analytics using R, Python, and Excel.

01

Data Understanding & Preparation

Grasp data types, dimensionality, and cleaning techniques essential for reliable analytics.

02

Excel for Data Analysis

Master spreadsheet functions, tables, formatting, and build simple dashboards.

03

Statistical Programming in R

Develop hands-on experience with R — from syntax to data analysis and regression models.

04

Python Programming for Analytics

Learn data handling, NumPy, Pandas, loops, visualizations, and basic machine learning.

05

Data Visualization Tools

Build insightful graphs and dashboards using Power BI, Seaborn, Matplotlib, and Shiny.

06

Applied Projects

Work on real-world projects like forecasting, classification, and NLP using learned tools.

Detailed Curriculum

Detailed Syllabus

Explore our comprehensive, step-by-step learning modules designed to build your expertise in data analytics using R, Python, and Excel. Each module combines theory with practical application to ensure deep understanding and real-world readiness.

  1. Meaning of Data and its practical relevance

  2. Types of measures in statistics

  3. Sources of data: primary and secondary

  4. Understanding data dimensionality

  5. Data description techniques

  6. Requirement gathering for analysis

  7. Data collection methods

  8. Data cleaning procedures for accurate modeling

  1. Introduction to Excel environment

  2. Data entry and formatting

  3. Creating and customizing tables

  4. Cell management and table properties

  5. Applying formulas and statistical functions

  6. Creating graphs and charts

  7. Building simple dashboards

  8. Mini project using Excel

  1. Setting up the R environment

  2. Understanding R syntax and basic operations

  3. Variables, data types, and decision structures

  4. Using vectors, lists, matrices, arrays

  5. Working with data frames and factors

  6. Loading and using R packages

  7. Reshaping and organizing datasets

  1. R data interfaces: file, database, web

  2. Importing/exporting data (CSV, Excel, etc.)

  3. Visualizing data with charts and graphs

    1. Concept of analysis and significance

    2. Calculating mean, median, mode

    3. Measuring standard deviation and variance

    4. Understanding data distribution

    5. Graphical representation using both R and Python

      1. Linear regression and interpretation

      2. Multiple regression modeling

      3. Poisson regression

      4. Quantile regression techniques and use cases

  1. Categorical variables and their significance

  2. Binary logistic regression

  3. Ordinal logistic regression

  4. Probit regression and interpretation

  1. Installing Python and setting up Jupyter Notebook

  2. Python basics: variables, types, and casting

  3. Operators and working with sequence data types

  1. Conditions and loops in Python

  2. Defining functions and control structures

  3. Using NumPy and Pandas for data manipulation

  4. Creating data frames using .iloc and .loc

  5. Joining datasets and visualizing using Matplotlib

  1. Seaborn for advanced data visualizations

  2. Descriptive statistics and distribution plots

  3. Building decision trees

  4. Basics of Artificial Neural Networks

  5. Applying SVM and Random Forest models

    1. Market Basket Analysis using ASM

    2. Principal Component Analysis (PCA)

    3. Factor Analysis (FA)

    4. Cluster Analysis (CA) techniques

  1. Introduction to Shiny in R

  2. Creating live dashboards for analytics reporting

  • Interns will complete practical projects in:

    1. Forecasting Analysis

    2. Decision Analysis

    3. Survival Analysis

    4. Association Rule Mining

    5. Business Models

    6. Surprise Learning

    7. Natural Language Processing (NLP)