Advance Data Science, Analytics and Machine Learning - Internship Assured

Data Manipulation

Objective:

Perform various data manipulation tasks using the Pandas library.

Concepts Used:

Loading data, cleaning and preprocessing, merging, grouping, and reshaping dataframes.

Steps:

• Cleaning and preprocessing data using Pandas

Feature Scaling and Transformation

Objective:

Apply feature scaling and transformation techniques to prepare data for machine learning models.

Concepts Used:

StandardScaler, MinMaxScaler, data normalization.

Steps:

• Using StandardScaler and MinMaxScaler

Visualization

Objective:

Create various types of plots and charts to visualize data trends and relationships.

Concepts Used:

Line plots, scatter plots, bar charts, histograms, heatmaps using Matplotlib and Seaborn.

Steps:

• Creating plots using Matplotlib and Seaborn

Basic Statistical Analysis

Objective:

Perform basic statistical analysis to understand data characteristics and distributions.

Concepts Used:

Mean, median, mode, standard deviation, variance, correlation, summary statistics.

Steps:

• Summary statistics and data characteristics

Applying EDA Techniques

Objective:

Apply various EDA techniques to understand data patterns, anomalies, and relationships.

Concepts Used:

Descriptive statistics, data visualization (histograms, box plots, scatter plots), missing data imputation, outlier detection, correlation matrices.

Steps:

• Using Python libraries (Pandas, Matplotlib, Seaborn) to perform EDA

Interpreting Results

Objective:

Draw meaningful insights and conclusions from EDA results to inform further analysis.

Concepts Used:

Interpreting visualizations, understanding statistical summaries, identifying key data characteristics.

Steps:

• Drawing insights from visualizations and statistical summaries

Creating Power BI Dashboards

Objective:

Design and build interactive dashboards in Power BI for data visualization and reporting.

Concepts Used:

Importing data, creating visuals, arranging layouts, adding filters and slicers.

Steps:

• Creating Power BI Dashboards

Performing Data Transformations

Objective:

Clean, transform, and reshape data using Power Query Editor to prepare it for analysis.

Concepts Used:

Renaming columns, changing data types, handling missing values, pivoting/unpivoting data, merging queries.

Steps:

• Performing Data Transformations

Using DAX for Advanced Data Analysis

Objective:

Write DAX formulas to create calculated columns, measures, and tables for advanced analysis.

Concepts Used:

Calculated columns, measures, time intelligence functions, filter context, row context.

Steps:

• Using DAX for Advanced Data Analysis

Connecting Power BI to Various Data Sources

Objective:

Connect Power BI to different data sources, including databases, Excel files, and web services.

Concepts Used:

Data connectors, import mode, direct query mode.

Steps:

• Connecting Power BI to Various Data Sources

Implementing and Evaluating Regression Models

Objective:

Implement and evaluate linear and polynomial regression models for predicting continuous outcomes.

Concepts Used:

Linear regression, polynomial regression, mean squared error (MSE), R-squared.

Steps:

• Building linear and polynomial regression models

Implementing and Evaluating Classification Models

Objective:

Implement and evaluate logistic regression, decision tree, and k-NN classifiers for predicting categorical outcomes.

Concepts Used:

Logistic regression, decision trees, k-nearest neighbors, accuracy, precision, recall, F1-score, confusion matrix, ROC curve.

Steps:

• Constructing logistic regression, decision tree, and k-NN classifiers

Implementing Naïve Bayes Classifiers

Objective:

Build and evaluate Naïve Bayes classifiers for classification tasks.

Concepts Used:

Gaussian Naïve Bayes, Multinomial Naïve Bayes, Bernoulli Naïve Bayes, probability, Bayes Theorem.

Steps:

• Using scikit-learn to build and evaluate probabilistic classifiers

Implementing SVM Classifiers

Objective:

Apply Support Vector Machine (SVM) for classification tasks using different kernel functions.

Concepts Used:

Linear SVM, Radial Basis Function (RBF) kernel, polynomial kernel, regularization, hyperplane.

Steps:

• Applying SVM for classification tasks using different kernel functions

Implementing Ensemble Algorithms

Objective:

Build and evaluate various ensemble models such as Random Forest, AdaBoost, and Gradient Boosting.

Concepts Used:

Bagging (Random Forest), Boosting (AdaBoost, Gradient Boosting), decision trees, aggregation.

Steps:

• Building and evaluating ensemble models (Random Forest, AdaBoost, Gradient Boosting)

Implementing Clustering and Dimensionality Reduction

Objective:

Apply K-means clustering, hierarchical clustering, and Principal Component Analysis (PCA) for data exploration and reduction.

Concepts Used:

K-means algorithm, dendrograms, linkage methods, elbow method, silhouette score, eigenvalues, eigenvectors, dimensionality reduction.

Steps:

• Using K-means, hierarchical clustering, and PCA for data exploration

Fine-tuning and Experimenting with LLMs

Objective:

Experiment with fine-tuning pre-trained Large Language Models (LLMs) and utilizing them for various text generation tasks.

Concepts Used:

Transfer learning, fine-tuning, Hugging Face Transformers library, OpenAI API, text generation, summarization, question answering.

Steps:

• Using OpenAI’s GPT models and Hugging Face transformers

Prompt Optimization

Objective:

Learn to design and optimize prompts for LLMs to achieve desired and improved AI responses.

Concepts Used:

Zero-shot, one-shot, few-shot prompting, chain-of-thought prompting, role-specific prompting, iterative testing.

Steps:

• Writing and testing various types of prompts for improved AI responses

Developing and Deploying ML Models

Objective:

Develop and deploy machine learning models as RESTful APIs using Flask.

Concepts Used:

Flask web framework, REST API principles (GET, POST), JSON data exchange, model serialization (pickle, joblib), API testing (Postman, curl).

Steps:

• Implementing machine learning models using Flask for REST API consumption

Excel Data Analysis

Objective:

Utilize MS Excel functions and features for data analysis, reporting, and dashboard creation.

Concepts Used:

Formulas, statistical functions (SUM, AVERAGE, COUNT, STDEV), logical functions (IF, AND, OR), lookup functions (VLOOKUP, HLOOKUP), pivot tables, charts, conditional formatting.

Steps:

• Using Excel functions for statistical and logical operations

SQL Querying

Objective:

Write SQL queries to interact with relational databases for data retrieval, manipulation, and reporting.

Concepts Used:

SELECT, FROM, WHERE, GROUP BY, ORDER BY, JOINs (INNER, LEFT, RIGHT, FULL OUTER), subqueries, CTEs, aggregate functions (COUNT, SUM, AVG, MIN, MAX).

Steps:

• Writing SQL queries for data extraction, transformation, and reporting

End-to-End Data Science Project

Objective:

Work on a comprehensive real-world data science project, covering all stages from data collection to model deployment and presentation.

Concepts Used:

Problem definition, data acquisition, data cleaning and preprocessing, exploratory data analysis, feature engineering, model selection, model training, evaluation metrics, hyperparameter tuning, model deployment, storytelling, presentation skills.

Steps:

• Working on a real-world data science project from data collection to model deployment

Interactive Problem-Solving Sessions

Objective:

Analyze and solve real-world data science and AI problems through interactive sessions and discussions based on case studies.

Concepts Used:

Critical thinking, problem identification, solution design, collaborative problem-solving, application of theoretical knowledge to practical scenarios, troubleshooting.

Steps:

• Analyzing real-world case studies and applying solutions