Contact Us
Data Sciences
Designed for learners to develop the skills needed to work with data and build intelligent solutions.
Duration: 6 Months
Mode: Online | Offline | Hybrid
Level: Beginner → Advanced
Projects: Data science exercises, Data-driven mini projects, Practical analysis tasks.
Certificate: Available. ISO Certified.
Career Options
Data Scientist
Machine Learning Engineer
Data Analyst (Advanced)
Business Intelligence (BI) Analyst
AI / Data Science Engineer (Entry Level)
Module 1 – Python Programming Core – 48 Hrs
- Introduction to Python: Python’s role in data analysis
- Data Types and Variables: Integers, floats, strings, and booleans
- Operators and Expressions: Arithmetic, logical, comparison, and assignment
- Control Flow: If-else statements, for and while loops
- Data Structures: Strings, tuples, dictionaries
- File Handling Basics: Reading and writing text files
- Functions and Modules: Writing reusable code and using built-in modules
- Error Handling: Managing errors with try-except blocks
Module 2 – Python Advanced: Key Libraries – 24 Hrs
- NumPy: Array creation, mathematical operations, indexing, reshaping
- Pandas: DataFrames, Series, importing/exporting, data cleaning
- Matplotlib: Bar charts, scatter plots, histograms, line graphs etc.
- Working with CSV and TXT Files: Reading, writing, and modifying structured data
Module 3 – SQL (Structured Query Language) – 24 Hrs
- Database Basics: Relational database concepts, RDBMS fundamentals
- SQL Queries: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Joins and Relationships: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
- Data Manipulation: INSERT, UPDATE, DELETE
- Constraints: Primary Key, Foreign Key, Unique, Check, Default, Not Null
- Subqueries and Set Operations: Subqueries, IN, EXISTS, UNION, INTERSECT
- Window (Analytic) Functions: ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG
- Administrative Commands: CREATE USER, ALTER USER, DROP USER, GRANT, REVOKE, COMMIT, ROLLBACK
Module 4 – Basic & Advanced Excel – 18 Hrs
- Basic Excel
- Formulas and Functions: SUM, AVERAGE, IF, VLOOKUP, HLOOKUP
- Data Formatting: Conditional formatting, tables, sorting
- Basic Charts: Bar, line, and pie charts
- Advanced Excel
- Pivot Tables: Creating and customizing pivot tables
- Data Analysis ToolPak: Regression, correlation, statistical analysis
- Macros and VBA: Automating tasks with simple scripts
Module 5 – Power BI – 10 Hrs
- Introduction to Power BI: Dashboards and reports overview
- Connecting Data Sources: Excel, SQL, APIs
- Data Transformation: Using Power Query
- Building Visualizations: Bar charts, pie charts, maps
- DAX (Data Analysis Expressions): Calculated columns, measures
- Interactive Dashboards: Creating dynamic, user-driven reports
- Publishing and Sharing: Making your reports accessible
Module 6: Python Libraries for Machine Learning (40 hrs)
- NumPy: Arrays, vectorized operations, indexing, reshaping
- Pandas: Series, DataFrames, filtering, groupby, merging, data cleaning
- Matplotlib: Line, bar, scatter, histogram plots
- Seaborn: Heatmaps, pairplots, distribution plots
- Scikit-learn: Overview of ML workflow
- Reading datasets from CSV files
- Basic data inspection using head(), info(), describe()
- Preparing data for ML models
Module 7: Introduction to ML , Data Preprocessing & Feature Engineering
Showcase Your Skills
- What is Machine Learning?
- AI vs ML vs Deep Learning
- Types of ML: Supervised, Unsupervised, Reinforcement
- Real-world ML applications
- Importing data using Pandas
- Exploratory Data Analysis (EDA)
- Missing value detection (.isnull())
- Statistical analysis (describe, correlation)
- Handling missing values: dropna(), fillna()
- Encoding categorical variables
- Feature scaling using StandardScaler
- Creating and selecting relevant features
Module 8 – Supervised Machine Learning
- Linear Regression (Simple & Multiple)
- Polynomial Regression
- Decision Tree Regressor
- Random Forest Regressor
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Decision Tree Classifier
- Random Forest Classifier
Module 9 – Unsupervised Machine Learning
- K-Means clustering
- Finding optimal number of clusters
- Cluster visualization
- Customer segmentation use case
- Hierarchical clustering
- Dendrogram visualization
- PCA (dimensionality reduction – intro)
- Apriori algorithm (association rules)
Module 10 – Model Evaluation & Validation& Model Deployment
- Train-test split
- Regression metrics: MAE, MSE, RMSE
- Classification metrics: Accuracy, Precision, Recall, F1-Score
- Confusion Matrix, ROC-AUC Curve
- Model score using .score()
- Overfitting & Underfitting
- Introduction to ML model deployment
- Saving & loading models (joblib / pickle)
- Handling user inputs & predictions
- Creating interactive ML apps using Streamlit& Flask
- Model testing & validation after deployment
Module 11 – Real-world Projects & Case Studies
- Netflix dataset analysis
- Data cleaning & visualization
- Outlier detection
- Titanic Prediction System
- Loan prediction system
- Diabetes prediction model
- Placement & salary prediction
- Customer segmentation project
FAQ
Who can join the Data Science course?
The course is suitable for students, graduates, professionals, and learners interested in data analysis and data-driven technology.
Do I need programming knowledge?
Basic programming knowledge can be helpful, but the required concepts can be developed during the program.
What technologies will I learn?
The program can include Python, SQL, Excel, data analysis tools, visualization, statistics, and other Data Science concepts.
Will I work with real datasets?
Yes. Practical datasets and exercises help learners understand how data is analyzed in real-world situations.
Does the course include Machine Learning?
Machine Learning concepts may be included depending on the selected Data Science program and curriculum.
What can I do after learning Data Science?
You can continue developing skills in areas such as Data Analysis, Machine Learning, AI, business analytics, and data visualization.
Are Data Science classes available online and offline?
Yes. TechnoTykes offers flexible learning options.
