# Essential Skills for Data Scientists in 2024

## ALL SKILLS NEEDED FOR DATA SCIENTISTS

### Machine Learning

**Classification**- Used for predicting categorical labels.
- Examples: Spam detection, sentiment analysis.

**Regression**- Used for predicting continuous values.
- Examples: House price prediction, stock price forecasting.

**Clustering**- Grouping similar data points together.
- Examples: Customer segmentation, image compression.

**Reinforcement Learning**- Training models through rewards and penalties.
- Examples: Game AI, robotics.

**Deep Learning**- Advanced subset of machine learning using neural networks.
- Applications: Image recognition, natural language processing.

**Dimensionality Reduction**- Reducing the number of random variables under consideration.
- Techniques: PCA, t-SNE.

### Programming

**Python/R**- Essential for data manipulation, analysis, and building machine learning models.
- Widely used libraries: pandas, NumPy, scikit-learn for Python.

**SQL**- Used for querying databases.
- Important for data extraction and manipulation.

### Data Visualization

**Tableau/PowerBI**- Tools for creating interactive and shareable dashboards.
- Useful for business intelligence and decision-making.

**Python Libraries for Data Visualization****Matplotlib**: Basic plotting.**Seaborn**: Statistical data visualization.**Plotly**: Interactive plots.**Geopandas**: Geographic data visualization.

### Data Analysis

**Feature Engineering**- Creating new features from raw data to improve model performance.

**Excel**- Fundamental tool for data analysis and visualization.
- Widely used for quick calculations and pivot tables.

**Data Wrangling**- Process of cleaning and unifying messy data sets.
- Techniques: Removing duplicates, handling missing values.

**EDA (Exploratory Data Analysis)**- Initial phase of data analysis to summarize main characteristics.
- Tools: Histograms, box plots, correlation matrices.

### IDE or Notebook

**Pycharm**- IDE for Python with advanced features.

**Spyder or R-Studio**- IDEs for Python and R respectively.

**Jupyter Notebook**- Web-based environment for interactive computing.
- Common for data science projects.

**Google Colab or Kaggle notebooks**- Cloud-based platforms for sharing and executing Python code.

### Maths

**Statistics & Probability**- Fundamental for understanding data distributions and making predictions.
- Applications: Hypothesis testing, confidence intervals.

**Linear Algebra**- Essential for machine learning algorithms.
- Topics: Matrices, vectors, eigenvalues.

**Differential Calculus**- Important for optimization problems.
- Applications: Gradient descent.

### Deploy

**AWS**- Popular cloud computing platform.
- Services: S3, EC2, Lambda.

**AZURE**- Microsoft's cloud platform.
- Services: Azure ML, Azure Functions.

### Web Scrapping

**Beautiful Soup**- Library for parsing HTML and XML documents.

**Scrappy**- Framework for web scraping and crawling websites.

**URLLIB**- Module for fetching data across the web.

**Reference:**

www.linkedin.com

Hamza Ali Khalid on LinkedIn: #datascientists #machinelearning ...

medium.com

Data Scientist Skillset: Top 23 Skills You Need to Master in 2024

theskilledmen.com

All Skills Needed for Data Scientist - The Skilled Men