Data Science For Beginner

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.

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.

Data Science for Beginner

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 the 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 behavioral pattern)
• It empowers your organization by allocating the best of 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 and also the chances of hiring a good candidate become more powerful.

*Even though, this is not an old game in the market but the scope is so high and it has been accepted in many sectors. 

Careers in Data Science

Data Science has been considered one of the sexiest 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 have 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, and Business Analyst.

Must Read – Data Science Roadmap

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

For more details, you can refer to – Data Science Life Cycle

Applications of Data Science

There are many applications of data science like – Search Engines, Transport, Finance, E-Commerce, Health Care, Image Recognition, Targeting recommendations, etc. For more details, you can refer to – Applications of Data Science

Python Basic

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

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

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

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

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

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

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 neighbors)
  • Hierarchal clustering
  • Anomaly detection
  • Principle Component Analysis
• Decision Tree
  • Decision Tree
  • Implementing Decision tree
  • Decision Tree Regression using sklearn

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

Natural Language Processing

• Introduction to Natural Language Processing
• Natural Language Processing
• Applications of NLP
• NLP Libraries
  • Scikit-learn
  • Natural language Toolkit (NLTK)
  • Pattern
  • TextBlob
  • Quepy
• 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

FAQs on Data Science Tutorials for Beginners

Q: 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.

Q: 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.

Q: 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++.

Q: 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.

Q: 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.

Q: Are there any ethical considerations in data science?

Answer: Yes, data ethics is an important consideration in the field of data science. This includes issues like data privacy, bias, and transparency in algorithms.

Q: Can I work on real-world data sets as a beginner?

Answer: Yes, working on real-world data sets can be a great way to apply your skills and gain experience. Look for publicly available data sets or seek out opportunities to work with data in your current job or personal projects.