Best Data Science Training in Mohali and Chandigarh
Complete Data Science Training in Python, Machine & Deep Learning, Advanced Statistics in Python, and Mathematics
This FUTURE FINDERS Data Science course includes unique hackathons, masterclasses, webinars, and Ask-Me-Anything sessions. The online training will give you hands-on experience with technologies like R, Python, Machine Learning, Tableau, Hadoop, and Spark. Improve your knowledge with this Data Science course and live interaction with other practitioners and Machine Learning Engineers.
To advance your career in data science, become a data scientist by delving deeply into the complexities of data interpretation, mastering technologies like machine learning, and developing strong programming abilities.
Syllabus for Data Science
- Day 1: Setup Python, Anaconda, Jupyter, Git
- Day 2: Python basics practice
- Day 3: Python functions, data structures
- Day 4: NumPy practice
- Day 5: Pandas: Series and DataFrames
- Day 6: Pandas: data cleaning
- Day 7: Pandas: groupby, pivot, merge
- Day 8: Date-time handling and feature creation
- Day 9: Matplotlib charts
- Day 10: Seaborn and Plotly
- Day 11: EDA project 1
- Day 12: SQL: SELECT, WHERE, GROUP BY
- Day 13: SQL joins and window functions
- Day 14: Python + SQL (SQLAlchemy)
- Day 15: Statistics lab: descriptive stats
- Day 16: Probability and distributions in Python
- Day 17: Hypothesis testing (t-test, chi-square)
- Day 18: Correlation and A/B testing lab
- Day 19: Outlier detection and imputation
- Day 20: Feature engineering and encoding
- Day 21: Scikit-learn pipeline and split
- Day 22: Linear regression project
- Day 23: Logistic regression and KNN
- Day 24: Decision tree, random forest
- Day 25: XGBoost/LightGBM
- Day 26: Evaluation metrics and cross-validation
- Day 27: Hyperparameter tuning
- Day 28: Imbalanced data (SMOTE)
- Day 29: Clustering: K-means, DBSCAN
- Day 30: PCA and feature selection
- Day 31: Time-series analysis and forecasting
- Day 32: NLP: text cleaning, TF-IDF, sentiment
- Day 33: Intro neural networks with Keras
- Day 34: Web scraping (BeautifulSoup)
- Day 35: APIs for data collection
- Day 36: Power BI/Tableau dashboard
- Day 37: Streamlit dashboard for model
- Day 38: Model deployment with FastAPI
- Day 39: Case study: sales/retail prediction
- Day 40: Capstone: problem definition and data collection
- Day 41: Capstone: cleaning and EDA
- Day 42: Capstone: modeling and tuning
- Day 43: Capstone: dashboard and deployment
- Day 44: Capstone: report and storytelling
Day 45: Final demo, GitHub, resume
- Week 1: Python, NumPy lab
- Week 2: Pandas cleaning and wrangling
- Week 3: Visualization and EDA project
- Week 4: SQL for data science
- Week 5: Statistics and hypothesis testing in Python
- Week 6: Feature engineering and ML basics
- Week 7: Regression and classification projects
- Week 8: Ensemble models, tuning, imbalanced data
- Week 9: Clustering, PCA, time series
- Week 10: NLP and deep learning intro
- Week 11: Dashboards (Power BI/Streamlit) and deployment
Week 12: Capstone project and demo
- Week 1: Python fundamentals
- Week 2: Python advanced and Git
- Week 3: NumPy and Pandas
- Week 4: Data cleaning projects
- Week 5: Visualization
- Week 6: EDA case studies
- Week 7: SQL basics
- Week 8: SQL advanced and Python-SQL
- Week 9: Statistics in Python
- Week 10: Hypothesis testing and A/B testing
- Week 11: Feature engineering
- Week 12: ML: regression
- Week 13: ML: classification
- Week 14: Ensemble models and tuning
- Week 15: ML project
- Week 16: Clustering and dimensionality reduction
- Week 17: Time-series forecasting
- Week 18: NLP
- Week 19: Deep learning with Keras
- Week 20: Big data intro: PySpark practice
- Week 21: Power BI/Tableau dashboards
- Week 22: Deployment: FastAPI, Streamlit, Docker
- Week 23: Capstone project
Week 24: Demo, portfolio, interview prep