Table of contents
- Programming Basics:
- Basic Statistics and Probability:
- Linear Algebra:
- Calculus:
- SQL and Database Management:
- Data Exploration and Visualization:
- Data Visualization with Tableau/Power BI:
- Data Wrangling and Preprocessing:
- Time Series Analysis:
- Supervised Learning:
- Unsupervised Learning:
- Deep Learning:
- Natural Language Processing (NLP):
- Reinforcement learning:
- Transfer learning:
- Model evaluation and optimization:
- Predictive modeling:
- Data storytelling:
Starting a journey in Data Science can be overwhelming, with questions about where to begin, what tools are necessary, and the proper learning sequence.
The following roadmap outlines a clear detailed path to answer these questions and provides guidance for your data science journey.
Programming Basics:
Tools: Python, R
Topics: Variables, data types, loops, functions, libraries, data structures, algorithms
Example project: Write a program to calculate the mean and standard deviation of a dataset
Basic Statistics and Probability:
Tools: Python, R, Excel
Topics: Descriptive Statistics, Inferential Statistics, Probability Distributions, Hypothesis Testing
Example project: Analyze survey data to determine the average income of a group of people
Linear Algebra:
Tools: Python (numpy), R (matrix)
Topics: Vectors, Matrices, Eigenvalues, Eigenvectors, Singular Value Decomposition
Example project: Implement a simple recommender system based on singular value decomposition
Calculus:
Tools: Python (sympy), R (rSymPy)
Topics: Differentiation, Optimization, Partial Derivatives
Example project: Derive the gradient of a cost function used in training a neural network
SQL and Database Management:
Tools: SQL, PostgreSQL, MySQL, SQLite
Topics: Queries, Joins, Aggregations, Normalization, Data Manipulation
Example project: Retrieve and analyze data from a database to find patterns and relationships in the data
Data Exploration and Visualization:
Tools: Python (Pandas, Matplotlib, Seaborn), R (ggplot2)
Topics: Data Cleaning, Summarization, Visualization, Exploration
Example project: Explore and visualize a dataset to gain insights into the data and find relationships and patterns
Data Visualization with Tableau/Power BI:
Tools: Tableau, Power BI
Topics: Interactive Data Visualization, Dashboarding, Data Storytelling
Example project: Create a dashboard in Tableau/Power BI to monitor business KPIs, or build an interactive data visualization to communicate insights and findings to stakeholders.
Data Wrangling and Preprocessing:
Tools: Python (Pandas, Numpy), R (dplyr, tidyr)
Topics: Data cleaning, Imputation, Normalization, Feature Extraction, Feature Scaling
Example project: Clean and preprocess a dataset to prepare it for modeling
Time Series Analysis:
Tools: Python (statsmodels, pandas), R (forecast)
Topics: Time series decomposition, ARIMA, Exponential smoothing, Time series forecasting
Example project: Forecast sales or stock prices using time series analysis techniques
Supervised Learning:
Tools: Python (scikit-learn), R (caret)
Topics: Linear regression, Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Support Vector Machines (SVM)
Example project: Build a prediction model to predict the prices of houses or classify images into different categories
Unsupervised Learning:
Tools: Python (scikit-learn), R (cluster)
Topics: K-Means Clustering, Hierarchical Clustering, Dimensionality Reduction (PCA)
Example project: Cluster customers based on their spending habits using K-means clustering or reduce the number of features in a dataset using PCA
Deep Learning:
Tools: Python (TensorFlow, PyTorch), R (keras)
Topics: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM)
Example project: Build a deep learning model to classify images of handwritten digits or to generate text using RNNs
Natural Language Processing (NLP):
Tools: Python (NLTK, spaCy), R (openNLP)
Topics: Text Preprocessing, Sentiment Analysis, Text Classification, Topic Modeling, Word Embeddings
Example project: Analyze customer reviews to determine the sentiment and identify the most frequently mentioned topics
Reinforcement learning:
Tools: Python (OpenAI Gym, TensorFlow), R (reinforcelearn)
Topics: Markov Decision Processes, Q-Learning, Monte Carlo Methods, Deep Reinforcement Learning
Example project: Train an agent to play a game or navigate a maze using reinforcement learning techniques
Transfer learning:
Tools: Python (Keras, PyTorch), R (keras)
Topics: Transfer Learning, Fine-Tuning, Pre-Trained Models
Example project: Use a pre-trained deep learning model for image classification and fine-tune it for a new dataset.
Model evaluation and optimization:
Tools: Python (scikit-learn), R (caret)
Topics: Evaluation Metrics (accuracy, precision, recall, F1 score), Model Selection, Hyperparameter Tuning, Cross-Validation
Example project: Evaluate and optimize a machine learning model to achieve the best performance on a given dataset
Predictive modeling:
Tools: Python (scikit-learn), R (caret)
Topics: Regression, classification, decision trees, random forests, support vector machines
Example project: Predict housing prices using linear regression or classify iris species using decision trees
Data storytelling:
Tools: Tableau, PowerBI, R (shiny)
Topics: Communicating insights effectively, choosing appropriate visualizations, creating interactive dashboards
Example project: Create an interactive dashboard to communicate the results of an analysis of customer data
If you are interested in learning only Data Analytics, the following topics can be skipped from the roadmap:
Programming Basics
Linear Algebra
Calculus
Deep Learning
Reinforcement Learning
Transfer Learning
Hope this helps you with your Data Science Journey! All the Best!! :D