Python Data Science Job Oriented Training In Bhopal

Fee: ₹18,000/- ₹30,000/-(Classroom training), ₹15,000/- ₹24,000/- (Online training) | Duration: 6 months

Class Modes: Offline and Online classes are available.

Interview Preparation & Placement Support

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Python Data Science Job Oriented Course Syllabus and Curriculum For 2025

Interview Preparation & Placement Support – "Learn, Practice, and Land Your Dream Job with Confidence!"

Fee: ₹18,000/- ₹30,000/-(Classroom training), ₹15,000/- ₹24,000/-(Online training) | Duration: 6 months

Class Modes: Offline and Online classes are available.

Course Content

Week 1: Introduction to Data Science

  • Overview of Data Science and its applications
  • Understanding the Data Science lifecycle
  • Introduction to tools (Python, Jupyter Notebooks, GitHub)
  • Data Science vs Data Analytics vs Machine Learning
  • Setting up the environment: Python Ecosystem, Jupyter Notebooks

Week 2: Python Basics

  • Python syntax and basic programming concepts (variables, loops, conditionals)
  • Functions and error handling
  • Introduction to Python libraries: NumPy, pandas, matplotlib

Week 3: Introduction to Data Structures and Data Types

  • Lists, Tuples, Sets, and Dictionaries in Python
  • Data manipulation with pandas: Series, DataFrames
  • Reading/writing data (CSV, Excel, JSON)

Week 4: Data Cleaning and Preprocessing

  • Handling missing data and outliers
  • Data type conversion and cleaning
  • Data normalization and scaling
  • Working with Date/Time data

Week 5–8: Data Manipulation with NumPy and Pandas

Week 5: Introduction to NumPy

  • NumPy arrays, indexing, and slicing
  • Array operations: addition, subtraction, multiplication, division
  • Broadcasting and vectorized operations

Week 6: Advanced NumPy

  • Multi-dimensional arrays
  • Matrix operations and linear algebra
  • Statistical functions with NumPy

Week 7: Pandas for Data Manipulation

  • Creating and manipulating Series and DataFrames
  • Indexing, filtering, and selecting data
  • Handling missing data in pandas

Week 8: Data Aggregation and Grouping

  • Grouping data and aggregating functions
  • Pivot tables and crosstabs
  • Merging, joining, and concatenating datasets

Week 9–12: Exploratory Data Analysis (EDA) and Visualization

Week 9: Descriptive Statistics and EDA

  • Measures of central tendency: mean, median, mode
  • Measures of spread: variance, standard deviation, IQR
  • Identifying patterns in data using EDA

Week 10: Data Visualization with Matplotlib

  • Basic plotting: Line, bar, scatter, and histograms
  • Customizing plots: titles, labels, legends, etc.
  • Subplots and multi-plotting

Week 11: Advanced Data Visualization with Seaborn

  • Creating heatmaps, pair plots, and distribution plots
  • Visualizing correlations, relationships, and distributions
  • Advanced plot types: Boxplots, violin plots, etc.

Week 12: Interactive Visualizations

  • Plotly and Bokeh for interactive visualizations
  • Customizing interactive charts
  • Dashboards and web-based visualization

Week 13–16: Introduction to Machine Learning

Week 13: Introduction to Machine Learning Concepts

  • Supervised vs. Unsupervised Learning
  • Overview of machine learning algorithms
  • Introduction to Scikit-learn library

Week 14: Linear and Logistic Regression

  • Linear regression for continuous variables
  • Logistic regression for classification problems
  • Evaluating regression models (MSE, RMSE, R²)

Week 15: Classification Algorithms

  • K-Nearest Neighbors (KNN)
  • Decision Trees and Random Forests
  • Evaluation metrics: Accuracy, Precision, Recall, F1-score

Week 16: Unsupervised Learning

  • Clustering: k-Means and Hierarchical Clustering
  • Dimensionality Reduction: PCA and t-SNE
  • Applications of clustering and dimensionality reduction

Week 17–20: Advanced Machine Learning and Model Evaluation

Week 17: Advanced Machine Learning Algorithms

  • Support Vector Machines (SVM)
  • Gradient Boosting and XGBoost
  • Neural Networks and Deep Learning (Introduction)

Week 18: Model Evaluation Techniques

  • Cross-validation techniques (K-fold cross-validation)
  • Hyperparameter tuning using GridSearchCV
  • Handling imbalanced datasets (SMOTE, Class Weights)

Week 19: Model Deployment

  • Model serialization using joblib and pickle
  • Creating REST APIs for models using Flask
  • Deploying machine learning models on the cloud (AWS/GCP)

Week 20: Model Monitoring and Optimization

  • Model monitoring techniques
  • Retraining models and concept drift
  • Model performance tracking

Week 21–24: Data Engineering and Web Development for Data Science

Week 21: Introduction to Big Data Tools

  • Overview of Hadoop and Spark
  • Introduction to cloud data engineering platforms (AWS, GCP, Azure)
  • Data pipelines in the cloud

Week 22: Working with Databases

  • SQL fundamentals and queries (SELECT, INSERT, UPDATE, DELETE)
  • NoSQL Databases: MongoDB
  • Connecting Python to databases (SQLite, PostgreSQL, MongoDB)

Week 23: Web Development for Data Science with Flask

  • Introduction to Flask framework
  • Building APIs with Flask
  • Creating a web app to interact with ML models

Week 24: Final Project and Evaluation

  • Hands-on project: Build a data science solution (e.g., predictive model, data dashboard)
  • Final assessment and feedback
  • Career guidance and interview preparation