3. Data Scientist:

Description: A Data Scientist applies statistical techniques, machine learning models, and AI to analyze complex data and derive predictive insights.

Responsibilities:

• Perform exploratory data analysis (EDA) to understand patterns.
• Develop and train machine learning models for predictive analytics.
• Interpret model results and communicate findings effectively.
• Conduct A/B testing and hypothesis testing.
• Work with unstructured data (text, images, video) for AI applications.

Required Skills:

• Python (Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch).
• Statistical analysis and probability.
• Machine learning and deep learning concepts.
• Natural Language Processing (NLP) and computer vision (optional).
• Data storytelling and visualization.

Essential Topics for Data Scientists

A strong foundation in these topics is critical for anyone pursuing a career in data science. The path includes programming, data manipulation, statistical analysis, machine learning, and working with big data technologies.

1. Programming Languages

• Python: Widely used across the industry for data science, machine learning, and deep learning applications.
• R Language: Preferred for statistical modeling and academic research.

Python Basics:

• Variables, Numbers, Strings
• Lists, Dictionaries, Sets, Tuples
• Control Structures: if, else, for, while
• Functions & Lambda Expressions
• Modules and Package Installation (pip install)
• File Handling: Reading/Writing Files
• Object-Oriented Programming: Classes and Objects

2. Version Control Systems

• Git & GitHub (Industry Standard)
• SVN (Apache Subversion)

3. Data Structures and Algorithms

• Core Structures: Lists, Sets, Dictionaries, Strings
• Advanced Structures: Stacks, Queues, Linked Lists, Trees, Heaps, Graphs
• Searching: Linear Search, Binary Search
• Sorting: Bubble Sort, Merge Sort, Quick Sort
• Recursion and Backtracking
• Graph Algorithms: BFS, DFS, Dijkstra’s Algorithm

4. Databases and SQL

• Relational Databases: PostgreSQL, MySQL
• NoSQL Alternatives: MongoDB

Basic Queries:

• SELECT, WHERE, LIKE, DISTINCT, BETWEEN, GROUP BY, ORDER BY

Advanced SQL:

• Subqueries, CTEs, Window Functions
• Indexing and Query Optimization

Joins:

• Inner Join, Left Join, Right Join, Full Outer Join

5. Mathematics and Statistics

• Types of Data: Continuous vs. Discrete, Nominal vs. Ordinal
• Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
• Visualization: Histograms, Bar Charts, Pie Charts, Scatter Plots
• Probability: Basics, Bayes Theorem
• Distributions: Normal, Binomial, Poisson
• Inferential Statistics: Confidence Intervals, Hypothesis Testing
• p-values, Z-test, t-test, ANOVA, Type I & II Errors
• Correlation & Covariance
• Central Limit Theorem

6. Data Preprocessing and Visualization

Libraries:

• NumPy (numerical operations)
• Pandas (data manipulation)
• Matplotlib & Seaborn (visualization)
• Scikit-Learn (preprocessing and ML)

Common Tasks:

• Missing value imputation
• Outlier detection and removal
• Encoding categorical variables
• Data scaling and normalization

7. Exploratory Data Analysis (EDA)

Steps in EDA:

• Load and inspect raw data
• Identify numerical vs. categorical features
• Detect and handle missing values
• Identify and treat outliers
• Handle imbalanced datasets
• Scale/normalize data
• Encode categorical variables
• Visualize data distributions and relationships

8. Machine Learning

(i) Data Preparation

• Handle Nulls and Outliers
• Normalize and Scale Data
• Feature Engineering and Selection
• Train-Test Split
• Cross Validation

(ii) Model Building

Types:

• Supervised: Regression, Classification
• Unsupervised: Clustering, Dimensionality Reduction

Algorithms:

• Linear Models: Linear Regression, Logistic Regression, Gradient Descent
• Tree-Based Models: Decision Tree, Random Forest, XGBoost

(iii) Model Evaluation Metrics

• Regression: MSE, MAE, RMSE, R², MAPE
• Classification: Accuracy, Precision, Recall, F1 Score, ROC-AUC, Confusion Matrix

(iv) Model Deployment

• Pipeline creation
• Model serialization (joblib, pickle)
• REST APIs with Flask/FastAPI
• Cloud deployment basics (AWS, Heroku, GCP)

9. Deep Learning

• What is a Neural Network?
• Activation Functions, Loss Functions
• Backpropagation and Optimizers
• Frameworks: TensorFlow, PyTorch
• Architectures: CNNs, RNNs, LSTMs

10. Advanced Topics

Natural Language Processing (NLP)

• Text Cleaning & Preprocessing
• Tokenization, Stopwords Removal, Lemmatization
• Bag-of-Words, TF-IDF, Word Embeddings
• Naïve Bayes, Logistic Regression
• NLTK, SpaCy, HuggingFace Transformers

Computer Vision

• Image Preprocessing: Resizing, Cropping, Normalizing
• Edge Detection, Filters
• Object Detection, Image Classification
• Data Augmentation Techniques

11. Big Data and Distributed Computing

• Hadoop Ecosystem: HDFS, MapReduce
• Apache Spark: PySpark, Spark SQL, MLlib
• Batch & Stream Processing Basics