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