Complete Road Map of Data Science

Phase 1: Foundations (3-4 months)

1. Math and Statistics

• Linear Algebra:

  Basics of Algebra required for data science

• Vector operations:

  Some of the basic Vector operations need to be learned for efficiency in data science

• Matrix operations:

  Basics of matrices are also required to understand patterns

• Eigenvalues and eigenvectors
• Calculus:
  • Limits: Required intermediate level
  • Derivatives: Derivatives are important for data science as they enable you to understand the dynamic behavior of data and changes in data
  • Integrals: Basic level
• Probability:

  As data science relies upon statistical techniques, you must be proficient in all related statistical concepts. Probability is one of them.

  • Random variables: Need to understand the behavior of random variables
  • Probability distributions: It enables you to understand the probability of occurrences of outcomes
  • Bayes' theorem: It is essential to calculate conditional probability
• Statistics:

  Statistics is an initial part of data science. No one masters DS without statistics. Here are some statistical techniques you must learn:

  • Descriptive statistics
  • Inferential statistics
  • Hypothesis testing

2. Programming

As with statistics, programming is a crucial part of DS. Here are some programming languages you need to master if you want to pursue a career as a data scientist:

• Python:
  • Basics (data types, control structures, functions)
  • NumPy (array operations, matrix operations)
  • Pandas (data manipulation, data analysis)
  • Matplotlib (data visualization)
• R:
  • Basics (data types, control structures, functions)
  • Data manipulation and analysis

3. Data Analysis

The third and attractive part of the course is data analysis, where you gain the ability to get valuable insights from data and use those visuals for decision-making. Data analysis involves the following techniques:

• Data preprocessing:
  • Handling missing values
  • Data normalization
  • Data transformation
• Data visualization:
  • Plotting (histograms, scatter plots, bar charts)
  • Visualization best practices
• Data manipulation:
  • Data merging and joining
  • Data grouping and aggregation

Phase 2: Data Science Essentials (4-6 months)

After the first four months of learning, you will be able to learn some more advanced essential tools and techniques for data science that will increase your efficiency more effectively. These include:

1. Machine Learning

• Supervised learning:
  • Regression (linear, logistic)
  • Classification (decision trees, random forests)
• Unsupervised learning:
  • Clustering (k-means, hierarchical)
  • Dimensionality reduction (PCA, t-SNE)
• Reinforcement learning:
  • Basics (agents, environments, rewards)
  • Q-learning and policy gradients

2. Data Modeling

Data Modeling is the process of creating and simplifying visual diagrams of text and symbols to represent data for observing how data flows.

• Regression:
  • Simple linear regression (When the variables are linearly related to each other in the case of one target and one feature variable)
  • Multiple linear regression (Apply multiple linear regression when multiple attributes are present in the model)
• Classification:
  • Logistic regression (Used when the outcome is in finite numbers like 1 or 2 and yes or no)
  • Decision tree and random forests (Both have their advantages and significance in DS. Random forests are more complex but more accurate than decision tree models)
• Clustering:
  • K-means clustering
  • Hierarchical clustering

3. Data Wrangling

Data Wrangling includes some must-learn techniques to become a data scientist:

• Data cleaning:
  • Handling missing values
  • Data normalization
  • Data transformation
  • Feature scaling
• Feature engineering:

Phase 3: Advanced Topics (4-6 months)

1. Deep Learning

Deep Learning is a technique that works like the human brain, creating neural chains to predict more effectively and efficiently.

• Neural networks:
  • Basics (perceptrons, multilayer perceptrons)
  • Activation functions (ReLU, sigmoid, tanh)
• Convolutional Neural Networks (CNNs):
  • Basics (convolutions, pooling)
  • Applications (image classification, object detection)
• Recurrent Neural Networks (RNNs):
  • Basics (recurrent connections, backpropagation through time)
  • Applications (sequence prediction, language modeling)
• Generative models:
  • Variational Autoencoders (VAEs)
  • Generative Adversarial Networks (GANs)

2. Big Data Technologies

Big Data technologies deal with datasets larger and more complex than those in traditional data analysis. Tools you need to master:

• Hadoop:
  • HDFS (Hadoop Distributed File System)
  • MapReduce (distributed computing model)
• Spark:
  • Basics (resilient distributed datasets, transformations, and actions)
  • Applications (data processing, machine learning)
• NoSQL databases:
  • MongoDB (document-oriented database)
  • Cassandra (column-family store)