Are you eager to embark on a journey into the exciting world of data science but feel overwhelmed by where to start? Fear not, for I've crafted a comprehensive roadmap that's tailor-made for total beginners, whether you come from a coding or computer science background or not. This roadmap not only outlines the technical skills you need but also highlights the soft skills that can set you on the path to success.
Data Science Roadmap for Beginners
Welcome to a transformative journey through the dynamic landscape of data science. In the age of information, where data reigns supreme, your curiosity has led you to the right place. You're about to embark on a guided expedition designed to empower beginners with the knowledge and skills to conquer the data-driven world. In the following pages, you will discover a comprehensive roadmap meticulously crafted to ensure your success, regardless of your starting point. Whether you're a coding prodigy or entirely new to the world of computer science, this roadmap is your compass in the realm of data science.
This isn't merely a roadmap; it's a gateway to a future where data insights hold the key to innovation, decision-making, and progress. Beyond just technical know-how, we'll emphasize the soft skills that will elevate you as a data scientist. So, fasten your seatbelt as we set forth on this exhilarating adventure. We'll decode the mysteries of data, unlock the power of algorithms, and navigate the complex waters of data science together. The data-driven future waits, and you're poised to be at the forefront of this transformative journey.
Prepare to embark on your path to shaping the data destiny. Welcome to the world of data science excellence.
Total Duration: 20 Weeks [5 Months]
Week 1 & 2: Data Science Foundation & Intro to. Python Programming
First Immersion into Data Science
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|-- Introduction to Data Science & its Significance
| |-- Data Sources
| |-- Data Cleaning, Preprocessing and Wrangling
| |-- Variables, Numbers, Strings (Fundamental Data Types)
| |-- Lists, Dictionaries, Set, Tuples (Data Types)
| |-- If condition, for loop, while loops (Conditionals)
| |-- Functions, modules
| |-- Read, write files
| |-- Exception handling
| |-- Classes, Objects
Week 3 - 5: Introduction to Statistical Concepts in Data Science
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|-- Common concepts include population
| |-- Variance
| |-- Covariance and Standard deviation
| |-- Regression
| |-- Skewness
| |-- Sample and Parameter
| |-- Analyzing categorical data
| |-- Displaying and comparing quantitative data
| |-- Summarizing quantitative data
| |-- Exploring bivariate numerical data
| |-- Inference for categorical data (chi-square tests)
| |-- Two-sample inference for the difference between groups
| |-- Counting, permutations, and combinations
| |-- Confidence intervals
| |-- Measures of Central tendency
| |-- Probability
| |-- Analysis of variance (ANOVA)
To use further statistics and probability resources - go to @ [Khan]
Week 6 & 7: Data Visualization in Python & R
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|-- Data Visualization using Excel
| |-- Data Visualization using Power Bi
| |-- Data Visualization using R
| |-- Learn Data Science libraries for Python
| |-- Numpy for Data Science
| |-- Pandas for Data Science
| |-- Learn Matplotlib or Seaborn in Python (Do not learn both)
| |--Register for a Kaggle Account and perform exploratory data analysis on at least 3 datasets @ [Kaggle]
Week 8 - 9: Structured Query Language (SQL)
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|--Topics
| |-- Basics of relational databases
| |-- Basic Queries: SELECT, WHERE LIKE, DISTINCT, BETWEEN, GROUP BY, ORDER BY
| |-- Advanced Queries: CTE, Sub queries, Window Functions
| |-- Joins: Left, Right, Inner, And Full
| |-- Stored procedures and functions
| |-- No need to learn database creation, indexes, triggers etc. as those things are rarely used by data scientists
Learning Resources
| | |-- Khan Academy: [Link]
| | |-- [w3schools SQL]
| | |-- [SQLBolt]
| | |-- SQL course for data professionals: [Link]
Week 10 – 14: Machine Learning **
|
|--Introduction to basic machine learning models **
| |-- Topics
| |-- Feature engineering
| |-- Linear Regression
| |-- Classification
| | |-- Binary Classification
| | |-- Multiclass Classification
| | |-- Multi-Label Classification
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|-- Machine Learning Concepts and Techniques
| |-- Decision Trees
| |-- Support Vector Machines
| |-- K- fold Cross Validation
| |-- K- Nearest Neighbors (KNN) Classification
| |-- Gradient Descent
| |-- Work on 5 Kaggle ML notebooks @ [Kaggle].
Week 13 - 15: Machine Learning Projects with Deployment.
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|-- Complete two end-to-end ML project:
| |-- Regression project: Complete the E-Commerce Data on [Kaggle] along with deployment to AWS or Azure.
| | |--Regression Project resources that could prove to be useful
| | |-- YouTube playlist link: [Link]
| | |-- Project covers following
| |-- Classification project: Complete the Explore Multi-Label Classification with an Enzyme Substrate Dataset on [Kaggle] along with deployment to AWS or Azure.
| | |-- Classification Project: Resources that could prove to be useful
| | |-- YouTube playlist link: [Link]
Week 16-20: Deep Learning **
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**Fundamentals of Deep Learning:
| |--Artificial neurons and their role.
| |--Activation functions (e.g., ReLU, sigmoid).
| |-- Basics of feed-forward neural networks (FNNs).
Model Training and Optimization:
| |-Gradient Descent and variants.
| |--Backpropagation algorithm.
| |--Learning rate and optimization techniques.
| |--Regularization
Recurrent Neural Networks (RNNs):
| |-- RNNs for sequence data.
| |--LSTMs and GRUs for sequential modeling.
| |--Time series analysis with RNNs.
Natural Language Processing (NLP):
| |--Tokenization and word embeddings.
| |--Text classification and sentiment analysis.
| |--Machine translation using RNNs.
Practical Data Science Projects:
| |--Hands-on data science projects.
| |-- Building and deploying deep learning models.
| |-- Model evaluation and performance metrics.
Deep Learning Frameworks:
| |-- Working with TensorFlow, PyTorch, or Keras.
| |-- Building data science models using deep learning libraries.
Ethical Considerations and Bias:
| |-- Ethical implications in data science.
| |-- Addressing fairness and bias in models.
Hyperparameter Tuning and Model Evaluation:
| |-- Techniques for model optimization.
| |-- Cross-validation and evaluation metrics.
Time Series Forecasting with Neural Networks:
| |-- Using RNNs and LSTM networks for time series prediction.
In closing, as you embark on this data science journey, always remember that the road to mastery is an ongoing adventure. With each step you take, you are not only acquiring new skills but also contributing to the ever-evolving world of data science. Stay persistent, embrace challenges, and never stop learning. The data science field is boundless, offering endless opportunities for innovation and discovery. Your dedication to this path will not only shape your own future but also have a profound impact on the world around you. Whether you're exploring data for the first time or adding advanced techniques to your repertoire, know that you are part of a vibrant and dynamic community of data enthusiasts. Share your knowledge, collaborate with others, and together, we'll continue pushing the boundaries of what's possible in the data-driven era. Your journey has just begun, and the possibilities are boundless. Here's to your success, your growth, and the exciting world of data science that awaits you. Safe travels, data explorers, and may your data-driven dreams come true.
Wishing you all the best on your data science voyage!
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