AI Data Analytics – 30 Days Internship

Join the AI Data Analytics 30 Days Internship at Acrosys Technologies and gain
practical experience in Python, data analysis, visualization, statistics,
machine learning, SQL, Power BI, Tableau and AI-based analytics projects.
This internship includes weekly mini projects, a final capstone project,
portfolio building and project presentation.

AI Data Analytics – 30 Days Internship Syllabus

Week 1: Python and Data Analytics Fundamentals

Day 1: Introduction to AI and Data Analytics

• Introduction to Artificial Intelligence, Machine Learning, Deep Learning and Data Analytics


• Types of analytics: Descriptive, Diagnostic, Predictive and Prescriptive


• Real-world AI and analytics applications


• Python installation and environment setup


• Tools: Python, Jupyter Notebook, Google Colab and VS Code

Day 2: Python Basics

• Variables and data types


• Input and output


• Operators


• Conditional statements


• Loops


• Functions


• Practice programs

Day 3: Python Data Structures

• Lists


• Tuples


• Sets


• Dictionaries


• String handling


• Mini exercises

Day 4: NumPy Basics

• Introduction to NumPy


• Arrays and indexing


• Array operations


• Mathematical functions


• Random number generation

Day 5: Pandas Fundamentals

• Introduction to Pandas


• Series and DataFrame


• Reading CSV and Excel files


• Selecting rows and columns


• Data inspection

Day 6: Data Cleaning

• Handling missing values


• Removing duplicate records


• Data formatting


• Data type conversion


• Introduction to outliers

Day 7: Mini Project 1

Project Options:

• Student Result Analytics


• Sales Data Analysis

Project Tasks:

• Load the dataset


• Clean the data


• Calculate statistics


• Generate reports

Week 2: Data Visualization and Statistics

Day 8: Data Visualization Basics

• Importance of data visualization


• Introduction to Matplotlib


• Introduction to Seaborn

Day 9: Charts and Graphs

• Line chart


• Bar chart


• Pie chart


• Histogram


• Scatter plot


• Box plot

Day 10: Advanced Visualization

• Heatmaps


• Pair plots


• Correlation matrix


• Dashboard concepts

Day 11: Statistics for Analytics

• Mean


• Median


• Mode


• Variance


• Standard deviation

Day 12: Probability Concepts

• Basics of probability


• Normal distribution


• Sampling


• Confidence intervals

Day 13: Exploratory Data Analysis

• Univariate analysis


• Bivariate analysis


• Correlation analysis


• Feature relationships

Day 14: Mini Project 2

Project Options:

• COVID Data Analytics


• IPL Match Analytics

Project Tasks:

• Perform exploratory data analysis


• Visualize trends


• Generate meaningful insights

Week 3: Machine Learning for Data Analytics

Day 15: Introduction to Machine Learning

• What is Machine Learning?


• Supervised and unsupervised learning


• Machine learning workflow

Day 16: Data Preprocessing

• Feature selection


• Label encoding


• One-hot encoding


• Train-test split


• Feature scaling

Day 17: Regression Algorithms

• Linear Regression


• Multiple Regression


• Regression performance metrics


• Understanding the equation: y = mx + b

Day 18: Classification Algorithms

• Logistic Regression


• K-Nearest Neighbours


• Decision Tree


• Random Forest

Day 19: Clustering Techniques

• K-Means Clustering


• Hierarchical Clustering


• Business use cases of clustering

Day 20: Model Evaluation

• Accuracy


• Precision


• Recall


• F1-Score


• Confusion Matrix

Day 21: Mini Project 3

Project Options:

• Customer Churn Prediction


• Loan Approval Prediction

Project Tasks:

• Train a machine learning model


• Evaluate model accuracy


• Generate predictions

Week 4: AI Analytics Projects and Deployment

Day 22: Introduction to AI Analytics

• AI in business intelligence


• Predictive analytics


• Recommendation systems


• Chatbots and AI dashboards

Day 23: Time Series Analytics

• Date and time handling


• Trend analysis


• Forecasting basics

Day 24: SQL for Data Analytics

• Database basics


• SELECT queries


• WHERE conditions


• GROUP BY


• JOIN operations

Day 25: Power BI and Tableau Basics

• Importing datasets


• Creating dashboards


• KPI cards


• Filters and slicers

Day 26: Deep Learning Introduction

• Neural network basics


• Perceptron


• Activation functions


• TensorFlow overview


• Introduction to the sigmoid function

Day 27: AI Analytics with Real-Time Data

• API data collection


• Web scraping basics


• Live dashboard concepts

Day 28: Resume and Portfolio Building

• GitHub profile setup


• LinkedIn profile optimization


• Internship project documentation


• Presentation preparation

Day 29: Final Capstone Project

Choose any one project:

• Sales Forecasting System


• AI-Based Business Dashboard


• Customer Segmentation


• Sentiment Analysis


• Fraud Detection


• Employee Performance Analytics

Day 30: Project Presentation and Viva

• Project demonstration


• Documentation submission


• Viva questions


• Career guidance


• Internship certificate evaluation

Tools Covered

• Python


• NumPy


• Pandas


• Matplotlib


• Seaborn


• Scikit-learn


• TensorFlow Basics


• SQL


• Power BI


• Tableau


• Jupyter Notebook


• Google Colab

Suggested Mini Projects

1. Student Performance Analytics


2. Sales Dashboard


3. COVID Trend Analysis


4. Movie Recommendation System


5. Sentiment Analysis


6. Customer Segmentation


7. Loan Prediction System


8. Employee Attrition Prediction

Internship Outcomes

After completing this internship, students will be able to:

• Analyse datasets professionally


• Create dashboards and reports


• Build machine learning models


• Perform predictive analytics


• Visualize business insights


• Develop AI-based analytics solutions


• Present real-time data analytics projects