Data Scientist ROADMAP to kick start your career

1. How to Become a
Data Scientist from
Scratch
by
SONU KUMAR

2. What is Data Science??
O Data Science as a multi-disciplinary subject encompasses the use
of mathematics, statistics, and computer science to study and
evaluate data. The key objective of Data Science is to extract
valuable information for use in strategic decision making,
product development, trend analysis and forecasting.
O A Data scientist is sort of 'jack-of-all-trades' for data crunching.
Basically, 3 main skills a data scientist needs to possess are
mathematics/statistics, computer programming literacy and
knowledge of particular business.

3. Data Science is a Broader Field

4. Comparison between Different Roles
in 2018

5. How to become a Data Scientist??
Math
Programming
Languages
Data Wrangling and
Management
Data Analysis and Visualization
Machine Learning
Deep Learning

6. Mathematics
O Linear Algebra: Matrix, Eigen, Tensor etc.
O Calculus: Differentiation and Integration.
O Probability: Bayes Theorem, Optimization etc.
O Statistics: Inferential Statistics, Descriptive Statistics, Chi
squared Testes, Random Variable, Gaussian And Normal
Distribution.
[Best Resources:- Khan Academy and Machine Learning
Mystery Mathematics Course]

7. Programming Languages
O Python: It is the Bible.
→ Easy to understand, i.e., plane English
→ No semicolon
→ Simple and tons of libraries available
O Talk about Packages
→ Data visualization using ggplot2, tidy are extremely important
[Best Resource :- Sentdex YouTube channel]

8. Libraries

9. Data Wrangling and Management
O Data Mining
O Data Cleaning
O Data Management
Relevant Skills:
→ MySQL: RDBMS
→ NoSQL: Mongo DB, Cassandra etc.
JOIN

10. Data Analysis and Visualization
O Plotting libraries in programming languages, e.g.,
• plotly, matplotlib, seaborn → python
• ggplot2 → R
• Tableau is booming now.
[Pandas and Numpy for Data Analysis]

11. Machine Learning and Deep Learning
O Domain Knowledge???
HEALTHCARE, BUSINESS, FINANCE, SPORTS etc.
Supervised Unsupervised Reinforcement

12. Machine Learning Algorithms
O Topics: Regression, Decision Tree, Random Forest, Naïve
Bayes, Ensemble Learning, AdaBoost, Hierarchical
Clustering, Association, k-means Clustering, SVM, KNN,
Gradient Descent, Cross Validation, Entropy, Accuracy,
Precision, Collaborative Filtration, PCA, Markov model,
Boltzmann theorem etc.
Testing Evaluation and Validation of Models

13. Deep Learning Algorithms
O Neural Networks, Feed Forward NN, Fuzzy Logic,
Sequence Model, LSTM, RNN, CNN, CapsNet, Time Series
etc

14. Big Data
O Map Reduce
O Hadoop
O Apache
O Spark
O Hive
O Pig
O Mahout
O Yarn

15. Additional Skills
NLP CV

16. Course Contents And Projects
O Introduction Data Mining
→ Introduction of Data Mining
→ Stages of the Data Mining Process
→ Data Mining Goals
→ Information and Knowledge
→ Advantages in Data Mining
→ Related technologies - Machine Learning, DBMS, OLAP, Statistics
→ Data Mining Techniques
→ Role of Data Mining in Various Field like Artificial Intelligence and
→ Internet of Things
→ Future scope of Data Mining

17. O Data Warehouse and OLAP/ Data preprocessing
→ Data cleaning
→ Data transformation
→ Data reduction
→ Data Warehouse and DBMS
→ Multidimensional data model
→ OLAP operations
O Machine Learning algorithms & concepts
→ Supervised and Unsupervised Technique
→ Regression Analysis
→ Linear Regression and Logistic Regression
→ Classification
→ Prediction

18. → Bayesian Classification Models
→ Association rules
→ Ensemble Learning
→ Neural Networks
→ Perceptron
→ MLP
→ SVM
O Python/Anaconda
→ Introduction to python and anaconda
→ Conditional Statements
→ Looping, Control Statements
→ Lists, Tuple ,Dictionaries
→ String Manipulation
→ Functions
→ Installing Packages

19. → Introduction of Various Tool
→ Introduction of Anaconda
O Working on Various Python Library
→ Installing library and packages for machine learning and data
→ science
→ Matplotlib
→ Scipy and Numpy
→ Pandas
→ IPython toolkit
→ scikit-learn
→ Tensorflow, Keras and other deep learning libraries
O Data Structures in Python
→ Intro to Numpy Arrays
→ Creating ndarrays
→ Indexing

20. → Data Processing using Arrays
→ File Input and Output
→ Sorting & Summarizing
→ Descriptive Statistics
→ Combining and Merging Data
O Data Analysis Using Pandas
→ Introduction to Pandas
→ Data Type of Pandas
→ Creating DataFrame using Pandas
→ Importing and Exporting Database
→ Working with Complex Data
→ Data Mining using Pandas .

21. O Hand on / Mini Projects on Data Sets
→ Modeling using Regression
→ Creating a Clustering Model
→ Loan Prediction Problem
→ Working on Iris Data Set
→ Titanic Data
→ Boston Housing Data Set
→ Predict Stock Prices
→ Classifying MNIST digits using Logistic Regression
→ Intrusion Detection using Decision
→ CIFAR Data set
→ ImageNet Data Set
→ Credit Risk Analytics using SVM in Python

22. Learning Outcomes
O Build artificial neural networks with Tensorflow and Keras
O Build Deep Learning networks to classify images with
Convolutional Neural Networks
O Implement machine learning, clustering, and search using TF/IDF
at massive scale with Apache Spark's MLLib
O Implement Sentiment Analysis with Recurrent Neural Networks
O Understand reinforcement learning - and how to build a Pac-Man
bot

23. O Make predictions using linear regression, polynomial
regression, and multivariate regression
O Implement Sentiment Analysis with Recurrent Neural
Networks
O Understand reinforcement learning - and how to build a
Pac-Man bot
O Classify medical test results with a wide variety of
supervised machine learning classification techniques
O Cluster data using K-Means clustering and Support Vector
Machines (SVM)

24. O Build a spam classifier using Naive Bayes
O Use decision trees to predict hiring decisions
O Apply dimensionality reduction with Principal Component
Analysis (PCA) to classify flowers
O Predict classifications using K-Nearest-Neighbor (KNN)
O Develop using iPython notebooks
O Understand statistical measures such as standard deviation
O Visualize data distributions, probability mass functions, and
probability density functions
O Visualize data with matplotlib

25. O Use covariance and correlation metrics
O Apply conditional probability for finding correlated
features
O Use Bayes' Theorem to identify false positives
O Understand complex multi-level models
O Use train/test and K-Fold cross validation to choose the
right model
O Build a movie recommender system using item-based and
user-based collaborative filtering
O Clean your input data to remove outliers
O Design and evaluate A/B tests using T-Tests and P-Values

26. Best Resources (Online Videos)
O Learn Python for Data Science by Microsoft → Edx
O Statistics and Probability by Khan Academy
O Introduction to Computing for Data Analysis → Edx
O Machine Learning for Data Science and Analytics → Edx
O Introduction to NoSQL Databases Solution → Edx
O Intro to Hadoop and Mapreduce → Coursera
[In Sequential order from Top]

27. Best Blogs and Open Source
Community
O Medium AI Community
O Freecodecamp
O Analytics Vidya
O Official Documentations
O Github and Stackoverflow
O Kaggle- Spend 5 hours of a day here
O Cheat Sheets from Amazon aws

28. Best Books
For Machine/ Deep Learning Data Science
Beginners
Book
Statistics

29. Overview of Data Science Tools and
Packages

30. Thank You