Data Scientist

Data Scientist

Builds statistical and ML models to extract insight and predictions from data.

⏱5 Months Duration

🏛️QP Code: SSC/Q8104

🏫Instructor: Tushar, Khushi

📅 Next batch: 1 July 2026·⚡ Limited seats — register early

About This Course

Builds statistical and ML models to extract insight and predictions from data.

🏛️NASSCOM / IT-ITeS SSC Certified

✅Government Recognised Certificate

⏱5 Months Program

💼Placement Support Included

📈Min 70% per NOS to Pass

🏫Live Projects & Portfolio

Course Curriculum

SSC/Q8104 · NSQF Level 6 · Assessment: Minimum 70% per technical NOS module.

Descriptive statistics: mean, median, mode, variance, standard deviation, percentiles
Probability fundamentals: Bayes theorem, conditional probability, distributions (normal, binomial, Poisson)
Inferential statistics: hypothesis testing, p-values, t-test, chi-square, ANOVA
Correlation and causation; A/B testing methodology

Python for data science: NumPy, pandas — data manipulation, cleaning, merging
Handling missing values, outliers, skewness, categorical encoding
Feature creation: polynomial features, interaction terms, domain-based features
Feature selection: correlation matrix, VIF, RFE, feature importance

Supervised: Linear/Logistic Regression, Decision Trees, Random Forest, XGBoost, SVM, KNN
Unsupervised: K-Means, DBSCAN, Hierarchical clustering, PCA, t-SNE
Model evaluation: confusion matrix, precision/recall/F1, ROC-AUC, RMSE, MAE, R²
Cross-validation (k-fold); hyperparameter tuning: GridSearchCV, RandomizedSearchCV

Neural network fundamentals: perceptron, activation functions, backpropagation
Deep learning with TensorFlow/Keras or PyTorch: CNN, RNN/LSTM basics
NLP: text preprocessing, TF-IDF, word embeddings, BERT overview; transfer learning

ML pipeline: data ingestion → preprocessing → training → evaluation → deployment
Model deployment: Flask API, Streamlit; Docker intro; MLflow for experiment tracking
Jupyter Notebooks; GitHub for code management; matplotlib/seaborn/Plotly for reporting

Key Learning Outcomes

Statistics: hypothesis testing, probability distributions
data wrangling and feature engineering (Python/pandas)
ML algorithms: supervised and unsupervised
model evaluation
deep learning basics
NLP intro
ML deployment

Tools & Technologies

Python scikit-learn pandas NumPy TensorFlow/PyTorch SQL Jupyter matplotlib/seaborn MLflow

Career Progression

Senior Data Scientist

ML Engineer

Frequently Asked Questions

What is Data Scientist and who is it for?

Data Scientist is a NASSCOM-certified course offered at White Banger Academy, Kaithal. It is designed for students, fresh graduates, and working professionals who want a government-recognised IT qualification to enter or grow in the technology sector.

Is NASSCOM certification valuable for jobs?

Yes. NASSCOM certifications are recognised by the Government of India and accepted by 500+ hiring partners across India. They are listed under the National Skill Qualification Framework (NSQF) and carry significant weight in IT hiring.

What is the course fee and are EMI options available?

Course fees depend on the batch and duration. White Banger offers flexible payment options including EMI. Please fill our enquiry form or WhatsApp us at +91 97283-20132 for the current fee structure.

Do you provide placement assistance after the course?

Yes. White Banger provides 100% placement support including resume building, mock interviews, and connections to our 500+ hiring partner network. Our placement rate is 92%.

What are the eligibility criteria and batch timings?

Most NASSCOM courses require Class 10 or 12 completion. Batches run in morning and evening slots (Monday to Saturday). The next batch starts 1 July 2026 — seats are limited, so early registration is recommended.