🔧 1. Python Programming
Data structures (lists, dicts, sets, tuples)
List comprehensions, lambda functions
Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn
📊 2. Statistics & Probability
Descriptive stats: mean, median, mode, variance
Probability distributions: Normal, Binomial, Poisson
Hypothesis testing (t-tests, p-values, confidence intervals)
Bayes’ Theorem
📈 3. Machine Learning Algorithms
Supervised: Linear/Logistic Regression, Decision Trees, SVMs, k-NN
Unsupervised: k-Means, Hierarchical Clustering, PCA
Ensemble: Random Forest, Gradient Boosting (XGBoost, LightGBM)
🔍 4. Model Evaluation Metrics
Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC
Regression: MAE, MSE, RMSE, R²
🔧 5. Feature Engineering
Handling missing data
Encoding categorical variables
Feature scaling (StandardScaler, MinMaxScaler)
Feature selection techniques
💡 6. Data Cleaning & Preprocessing
Handling outliers
Imputing missing values
Text cleaning (for NLP tasks)
📂 7. Data Analysis & Visualization
EDA using pandas, matplotlib, seaborn, plotly
Correlation analysis
Visual storytelling
🧠 8. Deep Learning (Basics)
Neural Networks: architecture, activation functions
CNNs and RNNs (basic understanding)
Frameworks: TensorFlow, PyTorch (optional for DS roles)
🏗️ 9. SQL & Databases
SELECT, JOINs, GROUP BY, HAVING, subqueries, window functions
Writing efficient queries
Normalization
🧠 10. Problem Solving & Case Studies
Business scenario interpretation
Data-driven decision-making
Common case study themes: churn prediction, A/B testing, fraud detection
⚖️ 11. A/B Testing & Experimentation
Hypothesis testing in experiments
Understanding control vs treatment
Significance levels and power
🧮 12. Linear Algebra (Basics)
Vectors, matrices, matrix multiplication
Eigenvalues and eigenvectors
Applications in PCA, ML models
💻 13. Algorithms & Data Structures (DSA)
Big-O complexity
Trees, graphs, stacks, heaps (if applying to tech-heavy DS roles)
🧰 14. Version Control (Git)
Basic Git commands: clone, commit, push, pull, merge
Using GitHub for code sharing
🌐 15. APIs & Web Scraping
Using requests, BeautifulSoup, or Selenium
Consuming REST APIs
Basic knowledge of JSON data handling
📦 16. Pipelines & MLOps (Optional but a plus)
Data pipelines: Airflow, Luigi
Model deployment basics: Flask, Docker, FastAPI
Model monitoring
🧑💼 17. Behavioral Interview Prep
STAR method (Situation, Task, Action, Result)
Tell me about yourself, strength/weakness, conflict resolution
Team collaboration and communication
📚 18. Portfolio & Projects
Kaggle competitions
End-to-end personal projects
GitHub repositories with clean code and documentation
🧑🏫 19. Explaining Complex Topics Simply
Practice explaining models like Random Forests to non-technical people
Use analogies, visuals, and storytelling
❓ 20. Mock Interviews & Practice
Leetcode for coding
Interview practice platforms: Interviewing.io, Pramp
Discussing projects and whiteboarding ML solutions
Learn Data Science Course in Hyderabad
Read More
Using Hugging Face for NLP Projects
MLflow for Machine Learning Experiment Tracking
How to Automate Data Science Workflows with Apache Airflow
Using Streamlit for Building Data Science Applications
Visit Our Quality Thought Training Institute in Hyderabad
Subscribe by Email
Follow Updates Articles from This Blog via Email
No Comments