Essential Skills for Data Scientists in 2024

Essential Skills for Data Scientists in 2024

ALL SKILLS NEEDED FOR DATA SCIENTISTS

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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