DATA SCIENCE

Data-Science: What is, Basics & Process

Path for Data Science

Programming

• Python_Programming
• SQL programming
• Excel
• Comfortable with using the Terminal, version control in Git, and using GitHub

Mathematics

• Calculus
• Linear Algebra
• Probability and Statistics:

Data wrangling:

• Accessing database, CSV, and JSON data
• Data cleaning and transformations using pandas

Data visualization with matplotlib

• visualization
• dashboards

Machine Learning

• Supervised Learning

  • Regression Algorithms
    • Linear Regression
  • Classification Algorithms
    • Logistic Regression
    • K-Nearest Neighbours
    • Naïve Bayes
  • Decision Tree or Random Forest Learning
  • SVM

• Unsupervised Learning

  • Clustering
    • K-Means
    • Agglomerative
  • Association Rule Mining
    • Apriori Algorithm
    • F-P Growth Algorithm
  • Dimensionality reduction
    • Principal Component Analysis
    • Kernel PCA

• Ensemble Methods

  • Simple Ensemble Techniques(In same Ensemble pages)
  • Advanced Ensemble techniques(In same Ensemble pages)
  • Bagging
    • Bagging meta-estimator(In same Bagging pages)
    • Random forest
  • Boosting
    • AdaBoost
    • GBM
    • XGBM

• Reinforcement Learning

Deep Learning

• Introduction to Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks

Recommendation Systems

• Content-Based and Collaborative Filtering
• Evaluation of Recommendation Systems. DCG, nDCG

Natural Language Processing

• Time Series Analysis
• Text Analytics
• Lexical processing
• Syntactic Processing
• Semantic Processing

Fundamentals of Big Data Engineering

• Hadoop and MapReduce Programming
• NoSQL Databases and Apache HBase
• Hive Tutorial
• Analytics using PySpark