Data Science Syllabus - Tools You should Learn to Become a Data Science Expert

The Data Science syllabus is very broad and confusing in terms of what to include and what not to include in the curriculum of the tasks to learn data science skills. So, it is categorized into tools, content, projects, and assignments you should know to learn data science skills.

Tools you should learn to become a data science expert

Python
Scikit Learn
Power BI
NumPy
Spark
Dask
Pytorch
Featuretools
Keras
Matplotlib
RASA
Pandas
ML Box
Streamlit
Flask
Mysql – SQL etc.

Content to be included in the Data Science syllabus

Master Microsoft Excel
Explore Important Formulas and Functions
Create Charts and Visualizations using MS Excel
Get Familiar with MySQL
Creating and updating reports in SQL
Performing Data Analysis using SQL
Explore Python for Data Science
Important libraries and functions in Python
Reading file and manipulating data in python
Working with data frames, lists, and dictionary
Use Matplotlib and Seaborn for data visualization
Creating charts to visualize data and generate insights
Univariate and Bivariate analysis using python
Perform Statistical Analysis on real-world datasets
Build and Validate Hypothesis using statistical tests
Generating useful insights from the data
Importing and working with different kinds of data in Tableau
Build bubble charts, geo-location charts, and many others
Learn to create Dashboards in Tableau
Master storyboarding in Tableau
Learn to create engaging presentations
Perform feature engineering in Tableau
Become familiar with data manipulation in Tableau
Loading datasets and establishing table relationships in PowerBI
Work with a different types of charts and dashboards in PowerBI
Working with Map visualizations and other advanced charts with drill-down functionalities in PowerBI
Working with power query for data manipulation in PowerBI
Writing DAX expressions in PowerBI
Dealing with ambiguous business problems
Structure a business problem into a data science problem
Understanding the Machine Learning Lifecycle
Key Frameworks for each stage in ML Lifecycle
Present analysis and business insights in an impactful manner
Communicate ideas and insights to the stakeholders
Learn Important Machine Learning concepts
Perform data cleaning and Preprocessing
In-depth understanding of Basic ML models
Linear Models, Decision Tree, k-NN
Math Behind each Machine Learning Algorithm
Building Classification and Regression Models
Hyperparameter Tuning to improve model
Solving real-world business problems using Machine Learning
Learn the art of Feature engineering
Feature Generation from time-series data
Automated Feature Engineering Tool
Concept of dimensionality reduction
Feature Selection and Elimination Techniques
Detailed Understanding of Principal Component Analysis (PCA)
Concept of Factor Analysis
Explore the Advanced ML concepts and Algorithms
Use Ensemble Learning Techniques (Stacking and Blending)
Understand and Implement Bagging and Boosting Algorithms
Learn to handle Text data and Image Data
Working with structured and unstructured data
Dealing with unsupervised learning problems
Clustering Algorithms including k-means and Hierarchical clustering
Important concepts of Deep Learning
Working of Neural Network from Scratch
Activation Functions and Optimizers for Deep Learning
Understand Deep Learning architectures (MLP, CNN, RNN, and more)
Explore Deep Learning Frameworks like Keras and PyTorch
Learn to tune the hyperparameters of Neural Networks
Build Deep Learning models to tackle real-life problems
Get familiar with the world of Computer Vision
Transfer Learning for Computer Vision
Work with popular Deep Learning Framework – Pytorch
Learn State-of-the-art Algorithms like YOLO, SSD, RCNN, and more
Work on different types of problems
Build Face Detection and Pose Detection Models
Advanced CV Problems like Image Segmentation and Image Generation
Understand how GANs work
Handling Text Data (Cleaning and Pre-processing)
Use Spacy, Rasa, and Regex for exploring and processing text data
Information Extraction and Retrieval from text-based data
Understand Language Modelling
Learn Advanced Feature Engineering techniques
Build NLP models for Text Classification
Understand Topic modeling
Work on Industry Relevant Projects
Understand the concept of Sequence-to-Sequence Modeling
Build a Deep Learning Model for Language translation in PyTorch
Learn to use the Transformers library by Huggingface
Use Transformers to perform transfer learning in NLP
Build and Deploy your own chatbot
Learn to work with audio-based data
Build a voice assistant system using Deep Learning
Recommender Systems in the industry
Detailed Taxonomy of types of Recommender Systems
Collaborative Filtering Methods
Content-Based Recommender Systems
Knowledge-Based & Hybrid Recommender Systems
Market Basket Analysis & Association Rules
Evaluation of Recommender Systems
Build Book recommender System and other real-life projects
Important concepts of Time Series Forecasting
Machine Learning techniques for Time Series forecasting
Validation techniques for Time series data
Framework to evaluate Time Series Models
Exponential Smoothing Methods for forecasting
Reading ACF and PACF plots
Tuning Parameters for ARIMA
ARIMA and SARIMA Model
Deep Learning for time series
Solve Real-world business problems
Understanding the different roles in Data Science
Dos and Don’ts for Resume Building
Tips and strategies to build the perfect resume
Preparing for Data Science Interviews
Understanding the important skills required
How to build your digital Presence
Tips and Tricks to Ace Data Science Resume
List of Interview Questions for Data Science
Overview of Python Basics
Documentation and Formatting
Testing and Debugging
Exception Handling and Assertions
Python Standard Libraries
Functional Programming
Object-Oriented Programming
Working with Shell/Terminal Commands
Introduction to Version Control and Github
Connecting with DataBases
Introduction to Hadoop and Spark Ecosystem
Deep Dive into Spark Ecosystem
RDDs in Spark
DataFrames in Spark
Spark SQL
Data Wrangling with Spark
Jobs, Stages, and Tasks in Spark
Advanced Programming in Spark
Machine Learning with Spark ML
Overview and aspects of Model Deployment
Deploying Machine Learning models using Streamlit
Introduction to Amazon Web services
Deploying and Machine Learning Deep Learning models using AWS
Understanding Amazon Sagemaker
Model Deployment using Sagemaker
APIs for Model deployment

Projects in the Data Science Syllabus

There can be any number of projects in the Data Science Syllabus, but it has to be more than 30+ projects in order to learn the data science skills like an expert. These projects are to be industry-specific only. Here is the list of some projects –

Auto Correct
Face Detection
Web Traffic Prediction
Movie Recommender System
Malaria Detection from blood cell Images
Webpage Classification
Customer Churn Prediction
NYC Taxi Trip Duration Prediction
Handwritten Digit Recognition
Gender and Age Detection master
Projects list can increase to any number.