How to Learn Data Science in 10 weeks?

The magic of “Data Science” has exploded in the entire market and has become a major wagon for all scales of businesses. Today, the decisions companies are making along with the forecast are solely dependent on data science. The field of data science has grown more than 3x folds, especially during the recent pandemic, as everyone was forced to work remotely, and thus it has opened many doors of opportunities for companies and working professionals to switch or start their careers in this field.

Recent stats have also suggested that almost 40% of companies have slashed their unwanted expenditure and that’s how more than 50,000 jobs have been generated in this field. Even if you talk about current stats, there are more than 1 lakh jobs available alone in India and more than 10 lakhs are there at a worldwide level. This clearly indicates that the scope in the field of data science is going to be glorious in the upcoming years. Now, the question arises is how to learn data science in 10 weeks? Learning could be fun and interactive only if you’ve chosen the right path and resources. In this article, we’ll see the steps, procedure, material, and allocation of time for learning data science right from the beginner’s level. Let’s find out:

But, before we move ahead, you must understand why you should opt for Data Science as a career point of view.

Why Choose Data Science?

It has become one of the hottest jobs out there in the market, as we’re going digital, more companies are relying on playing their major games by predicting the market forecast. Talking about the payout, it’s much better than most of the jobs out there in the market and it helps individuals to brush up on various skills such as mathematics, data visualization, analytics, etc.

Today’s scenario is that the demand for data science jobs is more but the candidates aren’t sufficient to fulfill those requirements and this has led to a major shift from a career perspective. Even employees are shifting their careers by learning data science. By this, you can assume how big the scope is and one thing is for sure, this demand is not going to decrease in the upcoming years.

Now, let’s get back to our today’s agenda and check out, How to Learn Data Science in 10 weeks? 

Week 1

Getting Started with Python

For those who don’t know Python is a high-level, multipurpose, cross-platform programming language that runs on multiple OS (such as Windows, Linux, and macOS) and it’s free to use. You can also go through the course Python Programming Foundation – Self-Paced and learn about Python Fundamentals.

So, the easiest way to start learning Python is to start within the following sequence:

• Introduction of Python
• Operators in Python
• Variables
• Data Types
• Conditions (if-else) (Python3- if, if-else, Nested-if)
• Loops
• Functions
• Data Structures (for Python)
• Exception Handling

You can also visit the Python IDE for best practices.

Week 2

Data Analysis with Python

After getting the basics of Python, it is a must to understand the core principles of data analysis, which is majorly used by companies today. All of the forecasts, predictions, and decisions that companies do make are solely based on data analysis patterns. For best practice, you can check out the Data Analysis with Python – Self-Paced course that has been tailed to equip you with logical and analytical handling. Now, to help you with this in detail, below is the list that you should consider in the next phase.

• What is Data Analysis
• Data Analysis Process – 6 Steps

Importing Data

1. How to import an Excel file in Panda?
2. How to read Text files in Python using Panda?
3. How to read JSON files with Panda?

Week 3

Data Visualization

• Overview of Data Visualization
• Data Visualization with Python (includes use cases of libraries such as Matplotlib, Seaborn, Bokeh, etc.)

Data Processing Methods

1. Understanding Data Processing
2. Pandas DataFrame
3. Data Cleaning (Overview)
4. Slicing, Indexing, Manipulating, and Cleaning Pandas Dataframe
5. Working with Missing Data in Pandas

Week 4

Exploratory Data Analysis

• EDA in Python – Set I (basic techniques to analyze the data)
• EDA in Python – Set II (basic visual techniques)
• EDA on Iris Dataset (explanation of EDA & techniques involved for data visualization)
• EDA using Seaborn – Titanic Dataset (explanation of EDA & techniques involved for data visualization)

Week 5

Web Scraping

• Introduction – Web Scraping (basics and its application)
• Types & How to use Web Scraping
• Python Web Scraping Tutorial

Project Guide for Web Scraping

1. Web Scraping from Wikipedia using Python
2. Scraping Amazon Product Information using Beautiful Soup
3. Amazon Customer Review
4. Scrape LinkedIn using Selenium & Beautiful Soup

Week 6

Mathematics

• Implementation of Mean, Variance, and Standard Deviation (in Python using NumPy)
• Derivative and Function minimization
• Probability Distribution
  • Set I – Uniform Distribution
  • Set II – Exponential Distribution
  • Set III – Normal Distribution
• Confidence Interval
• Covariance and Correlation
• Random Variables (With Examples)

Hypothesis Testing

• Basic Understanding
• T-testing
• Paired T-testing (detailed overview)
• P-value in ML
• F-Test
• Z-test

Mathematical Explanation in ML

• Chi-Squared Test for Feature Selection

ANOVA Test in Python

• ANOVA Test using one-way
• ANOVA using two-way

F-Test

• Introduction
• Implementation in Python

Week 7

Machine Learning

Machine Learning is one of the fanciest words that we hear these days which is also termed as new generation technology and has dominated this whole world in the era of technology. You name any device that exists today that is fully focused on AI, ML, and DL. Interestingly, the scope of this technology is also comparatively high in the market and the demand is going to explode by nearly 33% by the end of 2025. This is one of the reasons why people are shifting their careers to this field and they’re actively learning about this technology. If you wish to learn it from scratch, you may refer to Machine Learning Basic and Advanced – Self-Paced course that has been tailored to provide data processing methods in python to make you an industry expert. meanwhile, let’s look at the journey that you’re going to explore in the 7th week of data science.

Supervised Learning

• Regression and its types
• Classification
  • Random Forest
  • Decision Tree
  • Logistic Regression – Basic Understanding
  • Support Vector Machine Algorithm
• Neural Network – Beginner’s Guide

Unsupervised Learning

• K-means Clustering – Introduction
• DBscan Clustering – Density-Based Clustering
• KNN (k-nearest neighbors)
• Hierarchical Clustering (Agglomerative and Divisive clustering)
• Anamoly Detection
• Principal Component Analysis – PCA Introduction

Decision Tree

• Introduction
• How to implement a decision tree?
• Regression – Using Sklearn

Week 8

Deep Learning

• Basic Introduction
• Artificial Neural Network – Basic Introduction
• Convolutional Neural Network (CNN) 
• Recurrent Neural Network (RNN) – Introduction
• Generative Adversarial Network (GAN)
• Radial Basis Function Kernel
• Multi-Layer Perceptron Learning in Tensorflow
• Deep Learning with Python OpenCV

Project to Work 

• Pneumonia Detection using Deep Learning

Week 9

Natural Language Processing

• Overview of NLP
• Natural Language Understanding – NLU
• Natural Language Generation – NLG
• Future Scope of NLP

Project to work 

• Real-life applications of NLP

Natural Language Processing Libraries

• Scikit-Learn
• Natural Language Toolkit
• Pattern
• Textblob
• Quepy

Text Preprocessing

• Text Preprocessing in Python using NLTK – Set I
• Set II
• Syntax Tree

Featured – NLP

• Translation and NLP using Google Cloud
• NLP analysis of Restaurant reviews

Week 10

Since you’ve finished your journey right from Week 1 – Week 9, now is the time for you to take active participation to work in-depth in data science, this can be done by opting through Complete Data Science – LIVE course that will take you forward in becoming a class-apart data science expert. Whereas, take a look at some projects where you can understand the basics of data science and will definitely be going to help you to brush up on your skills. We have compiled a list of categorized projects/ideas for better clarity. Let’s have a look:

Data Analysis Project

• Data Analysis for Olympics: This project will drive you through various data and will show how you can implement and use best cases while giving tasks for data analysis.

Data Visualization Project

• Covid-19 Analysis and Visualization: This analysis project will help you with modeling, and showing visualization of the COVID-19 pandemic outbreak.

Web Scraping Project 

• How to Scrape Websites with Beautifulsoup and Python?: This project entails the use of web scraping for pulling out information from scratch (like traffic, stats, etc.) as per convenience. 

Machine Learning Projects (Beginners)

• Wine Quality Prediction: This project will work to predict the quality of wine as per the given stats.
• Credit card Fraud Detection: To identify the unauthorized transactions of any credit card
• Disease Prediction: This project model will help you with the early detection of any disease based on the input provided by the user.
• Sentence Prediction: This project will offer a prediction based on BERT that is capable to detect any language.

Machine Learning Projects (Advanced)

• Human Scream Detection: This project idea is an analysis for controlling the crime rate using machine learning and deep learning
• Fine-tuning the BERT model for Sentiment Analysis: This project uses a neural network layer for BERT architecture
• Detecting COVID-19 From Chest X-Ray Images using CNN: Three different machine learning models were used to build this project i.e. Xception, ResNet50, and VGG16.
• Face and Hand Landmarks Detection using Python – Mediapipe, OpenCV
• Age Detection
• AI-Driven Snake Game using Deep Q Learning
• Handwritten Digital Recognition: This project is built on Neural Network
• Predicting Stock Price Direction using Support Vector Machines

Deep Learning Projects

• Pneumonia Detection: Early detection of disease to provide medical care accordingly.
• Black and white image colorization: The change of color i.e. from grayscale to colored is simple by using this project idea.
• IPL Score Prediction: Cricket score prediction can be easily done with this project based on the inputs provided by the user.
• Wine-type Prediction: This project will drive you with Keras to understand the basics of neural networks.
• Human Activity Recognition – Using Deep Learning Model