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.

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