Data Science With Python

Our Data Science with Python training program is designed to help you master the complete lifecycle of data analysis, machine learning, and predictive modeling using one of the most powerful programming languages in the industry. Through hands-on projects and practical exercises, you will gain the skills needed to solve real-world business problems using data-driven approaches.

1. Python Programming for Data Science

  • Python basics: variables, loops, functions, data types

  • Working with libraries like NumPy, Pandas, Matplotlib, Seaborn

  • Writing clean, efficient, and reusable code

2. Data Wrangling & Data Preprocessing

  • Importing and cleaning raw datasets

  • Handling missing data, duplicates, and formatting issues

  • Feature engineering and feature transformation

  • Data normalization and standardization

3. Exploratory Data Analysis (EDA)

  • Understanding dataset structure and relationships

  • Summary statistics and descriptive analytics

  • Advanced data visualization techniques using Matplotlib & Seaborn

  • Identifying trends, patterns, and correlations

4. Data Visualization & Storytelling

  • Creating line charts, bar charts, histograms, scatter plots

  • Advanced dashboards and plots

  • Converting raw data into meaningful insights

  • Building visual reports useful for business decision-making

5. Machine Learning Fundamentals

  • Understanding supervised vs. unsupervised learning

  • Model training, testing, and validation

  • Algorithms covered:

    • Linear & Logistic Regression

    • Decision Trees & Random Forest

    • KNN, Naive Bayes

    • Support Vector Machine

    • Clustering techniques (K-Means, Hierarchical)

6. Model Evaluation & Optimization

  • Confusion matrix, accuracy, precision, recall, F1-score

  • ROC curves and AUC

  • Cross-validation

  • Hyperparameter tuning with GridSearch & RandomSearch

7. Working With Real Datasets

  • Business analytics datasets

  • Sales forecasting, customer segmentation

  • Financial risk prediction

  • Healthcare, retail, and e-commerce case studies

8. Introduction to Deep Learning (Optional)

  • Basics of Neural Networks

  • Using TensorFlow or Keras

  • Building simple deep learning models

9. Deployment & Real-World Application

  • Saving and deploying machine learning models

  • Introduction to Flask/Streamlit for model deployment

  • Working with APIs

10. Industry-Ready Projects

You will build multiple end-to-end projects such as:

  • Customer churn prediction

  • Fraud detection model

  • Sales prediction using regression

  • Sentiment analysis with NLP

  • Clustering customer segments

11. Interview & Certification Preparation

  • Common Data Science interview questions

  • SQL & statistics revision

  • Resume building for Data Science roles

  • Any Graduates(B.A,B.Com,B.Sc)
  • Engineering Students(B.Tech, B.E, M.Tech)
  • BCA,MCA
  • Any Diploma Holder
  • Any Working Professionals
  • Data Warehouse Administrators
  • Database Administrators
  • Software Tester
  • Project Manager
  • MIS Support

1. Introduction to Data Science

  • What is Data Science?

  • Data Science lifecycle

  • Applications across industries

  • Role of a Data Scientist

2. Python Programming Essentials

  • Python installation & environment setup

  • Variables, data types, operators

  • Conditional statements & loops

  • Functions, modules, and packages

  • Working with Jupyter Notebook

3. Data Handling & Manipulation

  • Introduction to NumPy arrays

  • Data wrangling with Pandas

  • Merging, grouping & filtering datasets

  • Handling missing data

  • Data cleaning best practices

4. Exploratory Data Analysis (EDA)

  • Descriptive statistics

  • Identifying trends & correlations

  • Outlier detection

  • Data summarization techniques

5. Data Visualization

  • Data plotting with Matplotlib

  • Advanced visualizations with Seaborn

  • Pair plots, heatmaps, histograms, box plots

  • Visual storytelling and insights presentation

6. Mathematics & Statistics for Data Science

  • Probability basics

  • Mean, median, mode, variance, standard deviation

  • Hypothesis testing

  • Correlation & covariance

  • Linear algebra basics for ML

7. Machine Learning Fundamentals

  • Supervised vs. Unsupervised learning

  • Train-test split, model training & evaluation

  • Feature engineering & feature scaling

8. Supervised Machine Learning Algorithms

  • Linear Regression

  • Logistic Regression

  • Decision Trees & Random Forest

  • K-Nearest Neighbors (KNN)

  • Support Vector Machines (SVM)

  • Naive Bayes

9. Unsupervised Machine Learning Algorithms

  • K-Means Clustering

  • Hierarchical Clustering

  • PCA (Principal Component Analysis)

10. Model Evaluation & Optimization

  • Accuracy, precision, recall, F1-score

  • ROC & AUC

  • Confusion matrix analysis

  • Cross-validation

  • Hyperparameter tuning (Grid Search & Random Search)

11. Introduction to Deep Learning (Optional Track)

  • Basics of Neural Networks

  • Activation functions

  • TensorFlow/Keras basics

  • Building simple neural models

12. Natural Language Processing (Optional Track)

  • Text preprocessing

  • Tokenization & stop words

  • Sentiment analysis

  • TF-IDF / Bag-of-Words model

13. Working with Real-World Datasets

  • Business analytics datasets

  • Finance, retail, healthcare case studies

  • Kaggle data projects

14. Model Deployment

  • Saving & loading ML models

  • Deploying models with Flask or Streamlit

  • Creating prediction APIs

15. End-to-End Capstone Projects

Examples include:

  • Customer churn prediction

  • Sales forecasting

  • Fraud detection system

  • Sentiment analysis

  • Movie recommendation model

16. Interview & Career Preparation

  • Data Science interview questions

  • Python coding challenges

  • Resume building for DS roles

  • Portfolio & GitHub guidance

  • Develop a strong foundation in Python programming for data analysis and machine learning.

  • Learn to clean, preprocess, and transform raw data into meaningful datasets.

  • Perform Exploratory Data Analysis (EDA) to uncover patterns, trends, and insights.

  • Master essential data science libraries such as NumPy, Pandas, Matplotlib, and Seaborn.

  • Understand and apply key statistical and mathematical concepts used in data science.

  • Build predictive models using supervised and unsupervised machine learning algorithms.

  • Evaluate and optimize machine learning models using industry-standard metrics.

  • Create professional data visualizations to communicate insights effectively.

  • Develop end-to-end real-world data science projects for portfolio building.

  • Learn to deploy machine learning models using Flask or Streamlit.

  • Gain practical experience working with real datasets from multiple domains.

  • Prepare for Data Science interviews and job roles such as Data Analyst, Data Scientist, and ML Engineer.

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