Data Science for Beginners

Data Science is a domain that comprises many sub-domains such as artificial intelligence, machine learning, statistics, data visualization, and analytics as well as provides practical examples and exercises to help you apply these concepts in the real world. Over the past few years, there has been tremendous demand for data scientists. To improve business efficiency it becomes important to analyze the data.

In this data science tutorial, we will provide a comprehensive overview of the core concepts, tools, and techniques used in the field of data science.

Data Science is a field that involves extracting insights and knowledge from data using various techniques and tools. If you are a beginner in Data Science, here are some steps you can follow to get started:

1. Learn Programming: Programming is a fundamental skill for Data Science. Python is the most commonly used programming language in Data Science, and it has several libraries that are useful for Data Science, such as NumPy, Pandas, and Scikit-learn. You can start by learning the basics of Python programming.
2. Learn Statistics: Statistics is the foundation of Data Science. Understanding statistical concepts such as mean, median, variance, and standard deviation is crucial for working with data. You can start by learning the basics of statistics.
3. Learn Data Visualization: Data visualization is an essential skill for Data Science. It helps to understand patterns and trends in data. There are several libraries in Python that are useful for Data Visualization, such as Matplotlib and Seaborn.
4. Learn Machine Learning: Machine learning is the core of Data Science. It involves building models that can learn from data and make predictions. There are several types of machine learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning. You can start by learning the basics of machine learning.
5. Practice with Projects: Practice is essential for learning Data Science. You can start by working on small projects such as data cleaning, data analysis, and machine learning models. Kaggle is a platform where you can find data science projects and competitions to practice your skills.
6. Learn from the Community: The Data Science community is very active, and there are several resources available to learn from. You can join online communities such as Reddit, LinkedIn, or Twitter. You can also attend local Data Science meetups and events.
7. Continuously Learn: Data Science is a rapidly evolving field, and new techniques and tools are constantly emerging. Therefore, it’s essential to keep learning and stay updated with the latest trends and developments in Data Science.

In summary, learning Data Science involves programming, statistics, data visualization, machine learning, practice, learning from the community, and continuous learning. With dedication and consistent effort, you can become proficient in Data Science and start building solutions to real-world problems.

By the end of this tutorial, you’ll have a solid understanding of the key concepts and tools used in data science for beginners, and be well on your way to becoming proficient in the field.

Need for Data Science

There are 4 major reasons why there is a need for data science in the existing world today.

• Businesses are running today based on customer insight and that’s where data science comes from. With the help of data science, companies use Data Mining and sorting techniques to understand the area of interest of their users.
• Today, data science is being actively used to trim unstructured and unorganized data that also consumes less time.
• It helps in identifying the objective of a business and helps in reaching the goal (meanwhile it also helps in predicting the futuristic data based on the behavioural pattern)
• It empowers your organization by allocating the best of the best people within your workforce. It helps in sorting and filtering out the candidates from different platforms and that proportionally saves a lot of time also the chances of hiring a good candidate become more powerful.

Careers in Data Science

Data Science has been considered one of the most desirable jobs in the IT field today. The growth opportunities in data science jobs are comparatively high than in any other job. Companies are now focusing more on data science jobs to elevate their business goals which has also created a flood of data science jobs in the market.  

Some of the most notable jobs in data science are:- 

•  Data Scientist,
•  Data Architect,
•  Data Administrator,
•  Data Analyst, 
•  Business Analyst.

Data Science Life Cycle

It is a methodology followed to solve the data science problem.

• Business Understanding
• Data Understanding
• Preparation of Data
• Exploratory Data Analysis
• Data Modeling
• Model Evaluation
• Model Deployment

Applications of Data Science

There are many applications of data science are as follows:- 

• Search Engines, 
• Transport, Finance,
•  E-Commerce, 
• Health Care, 
• Image Recognition,
•  Targeting recommendations, etc.

Prerequisites & Tools for Data Science

To gain expertise in the field of data science. firstly, you need to have a strong foundation in various aspects of data science. which includes knowledge of query languages like:- SQL, programming languages like R and python, and as well as visualization tools like:- PowerBI, Quilsense, Quilview, and Tableau. Additionally, having a basic understanding of statistics for machine learning is crucial. To effectively apply machine learning algorithms, it is essential to practice and implement them with use cases relevant to your desired domain.

Section 1: Python Basic

• Introduction of Python
• Taking input in Python
• Variables
• Operators
• Data Types
• Conditions
• Loops
• Functions
• Object-Oriented Programming
• Exception Handling

Section 2: R Basic

• Introduction to R Programming Language
• Operators
• Keywords
• Data Types
• Decision Making – if, if-else, if-else-if ladder, nested if-else, and switch
• Loops (for, while, repeat)
• Functions
• Introduction to Object-Oriented Programming
  • Classes
  • Objects
  • Encapsulation
  • Polymorphism
  • Inheritance

Section 3: Data Analysis with Python

• What is Data Analysis
• Data Analysis using Python
• Steps of Data Analysis Process
• Importing Data
  • Import Excel file with Pandas
  • Import Text file with Pandas
  • Read JSON Files with Pandas
• Data processing
  • Pandas DataFrame
  • Overview of Data Cleaning
  • Slicing, Indexing, Manipulating and Cleaning Pandas Dataframe
  • Working with Missing Data in Pandas
  • Identify and Handle Missing Values
• Data visualization
  • Why is It Important?
  • Data Visualization using Matplotlib
  • Style Plots using Matplotlib
  • Line chart in matplotlib
  • Bar Plot in Matplotlib
  • Box Plot in Python using matplotlib
  • Scatter Plot in Matplotlib
  • Heatmap in Matplotlib
  • Three-dimensional Plotting using Matplotlib
  • Seaborn Kdeplot
  • Data Visualization with Python Seaborn
  • Interactive Data Visualization with Bokeh
  • Time Series Plot or Line plot with Pandas
• Exploratory Data Analysis
  • Set 1
  • Set 2
  • Exploratory Data Analysis on Iris Dataset
  • Exploratory Data Analysis on Titanic Dataset

Section 4: Data Analysis with R

• Importing Data
  • Importing Data in R Script
  • Import Data from a File in R
  • Import a CSV File into R
• Data processing using R
  • Data Frames in R
  • DataFrame Manipulation
  • Data Cleaning in R
  • Working with Missing Data in R
• Data visualization using R
  • Data visualization with R and ggplot2
  • Scatter plots in R Language 
  • Graph Plotting in R Programming 
  • Visualizing Missing Data with Barplot in R
  • Histograms in R language
  • Boxplots in R Language
  • Time series visualization with ggplot2 in R
• Exploratory Data Analysis in R

Section 5: Web Scraping

• Introduction to Web Scraping
• What is Web Scraping and How to Use It?
• Web scraping with Python
  • Web scraping from Wikipedia using Python
  • Scraping Amazon Product Information using Beautiful Soup
  • Web Scraping – Amazon Customer Reviews
• Scrape LinkedIn Using Selenium And Beautiful Soup in Python
• Web Scraping with R
  • Extract all the URLs from the webpage Using R Language

Section 6: Basic Stat Mathematics

• Mean, Standard Deviation and Variance — Implementation
• Derivative and Function minimization
• Probability Distributions[Set 1, Set 2, Set 3]
• Confidence Intervals
• Correlation and Covariance
• Random Variables
• Hypothesis Testing
  • T-test
  • Paired T-test
  • p-value
  • F-Test
  • z-test
• Chi-squared Test
• ANOVA Test
  • ANOVA Test using Python[One-way, Two-way]
  • ANOVA Test using R
• F-Stats
  • F-Stats With Python
  • F-Stats With R

Section 7: Machine Learning

• Supervised Learning
  • Regression
    • Linear Regression
    • Regression Trees
    • Non-Linear Regression
    • Bayesian Linear Regression
    • Polynomial Regression[Using Python, Using R]
  • Classification
    • Random Forest
    • Decision Trees
    • Logistic Regression
    • Support Vector Machines
  • Neural Networks
• Unsupervised Learning
  • K-means clustering
  • DBScan clustering
  • KNN (k-nearest neighbours)
  • Hierarchal clustering
  • Anomaly detection
  • Principle Component Analysis
• Decision Tree
  • Decision Tree
  • Implementing Decision tree
  • Decision Tree Regression using sklearn

Section 8: Deep Learning

• Introduction to Deep Learning
• Introduction to Artificial Neutral Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks
• Generative Adversarial Networks (GANs)
• Radial Basis Function Networks (RBFNs)
• Multilayer Perceptrons (MLPs)
• Deep Learning with Python OpenCV
• Pneumonia Detection using Deep Learning

Section 9: Natural Language Processing

• Introduction to Natural Language Processing
• Natural Language Processing
• Applications of NLP
• NLP Libraries
  • Scikit-learn
  • Natural language Toolkit (NLTK)
  • Pattern
  • TextBlob
  • Query
• Text Preprocessing in Python | Set – 1
• Text Preprocessing in Python | Set 2
• Syntax Tree – Natural Language Processing
• Translation and Natural Language Processing using Google
• NLP analysis of Restaurant reviews

Some project Ideas for Beginner in Data Science – link

FAQs on Data Science Tutorials for Beginners

Q1: What is data science?

Answer: 

Data science is a field that involves using techniques from statistics, mathematics, and computer science to analyze and draw insights from data.

Q2: What skills do I need to be a data scientist?

Answer: 

Data scientists typically need skills in statistics, machine learning, data visualization, and programming. Strong communication and critical thinking skills are also important.

Q3: What programming languages should I learn for data science?

Answer: 

Some popular programming languages for data science include Python, R, and SQL. It’s also helpful to have some familiarity with other languages like Java and C++.

Q4: How long does it take to learn data science?

Answer: 

Learning data science is an ongoing process that can take several months to several years, depending on your background and level of experience.

Q5: What kind of jobs can I get with a background in data science?

Answer: 

Some common job titles in data science include data analyst, data scientist, machine learning engineer, and business intelligence analyst.