Learn Data Science With Python

This data science with Python tutorial will help you learn the basics of Python along with different steps of data science according to the need of 2023 such as data preprocessing, data visualization, statistics, making machine learning models, and much more with the help of detailed and well-explained examples. This tutorial will help both beginners as well as some trained professionals in mastering data science with Python.

What is Data Science

Data science is an interconnected field that involves the use of statistical and computational methods to extract insightful information and knowledge from data. Python is a popular and versatile programming language, now has become a popular choice among data scientists for its ease of use, extensive libraries, and flexibility. Python provide and efficient and streamlined approach to handing complex data structure and extracts insights.

• Introduction
• Python Basics
• Data Processing
• Data Visualization
• Statistics
• Machine Learning
• Natural Language Processing

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Introduction

• Introduction to Data Science
• What is Data?
• Python for Data Science
• Python Pandas
• Python Numpy
• Python Scikit-learn
• Python Matplotlib

Python Basics

• Taking input in Python
• Python | Output using print() function
• Variables, expression condition and function
• Basic operator in python
• Data Types
  • Strings
  • List
  • Tuples
  • Sets
  • Dictionary
  • Arrays
• Loops
• Loops and Control Statements (continue, break and pass) in Python
• else with for
• Functions in Python
• Yield instead of Return
• Python OOPs Concepts
• Exception handling

For more information refer to our Python Tutorial

Data Processing

• Understanding Data Processing
• Python: Operations on Numpy Arrays
• Overview of Data Cleaning
• Slicing, Indexing, Manipulating and Cleaning Pandas Dataframe
• Working with Missing Data in Pandas
• Pandas and CSV
  • Python | Read CSV
  • Export Pandas dataframe to a CSV file
• Pandas and JSON
  • Pandas | Parsing JSON Dataset
  • Exporting Pandas DataFrame to JSON File
• Working with excel files using Pandas
• Python Relational Database
  • Connect MySQL database using MySQL-Connector Python
  • Python: MySQL Create Table
  • Python MySQL – Insert into Table
  • Python MySQL – Select Query
  • Python MySQL – Update Query
  • Python MySQL – Delete Query
• Python NoSQL Database
• Python Datetime
• Data Wrangling in Python
• Pandas Groupby: Summarising, Aggregating, and Grouping data
• What is Unstructured Data?
• Label Encoding of datasets
• One Hot Encoding of datasets

Data Visualization

• 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
• Time Series Plot or Line plot with Pandas
• Python Geospatial Data
• Other Plotting Libraries in Python
  • Data Visualization with Python Seaborn
  • Using Plotly for Interactive Data Visualization in Python
  • Interactive Data Visualization with Bokeh

Statistics

• Measures of Central Tendency
• Statistics with Python
• Measuring Variance
• Normal Distribution
• Binomial Distribution
• Poisson Discrete Distribution
• Bernoulli Distribution
• P-value
• Exploring Correlation in Python
• Create a correlation Matrix using Python
• Pearson’s Chi-Square Test

Machine Learning

Supervised learning

• Types of Learning – Supervised Learning
• Getting started with Classification
• Types of Regression Techniques
• Classification vs Regression
• Linear Regression
  • Introduction to Linear Regression
  • Implementing Linear Regression
  • Univariate Linear Regression
  • Multiple Linear Regression
  • Python | Linear Regression using sklearn
  • Linear Regression Using Tensorflow
  • Linear Regression using PyTorch
  • Pyspark | Linear regression using Apache MLlib
  • Boston Housing Kaggle Challenge with Linear Regression
• Polynomial Regression
  • Polynomial Regression ( From Scratch using Python )
  • Polynomial Regression
  • Polynomial Regression for Non-Linear Data
  • Polynomial Regression using Turicreate
• Logistic Regression
  • Understanding Logistic Regression
  • Implementing Logistic Regression
  • Logistic Regression using Tensorflow
  • Softmax Regression using TensorFlow
  • Softmax Regression Using Keras
• Naive Bayes
  • Naive Bayes Classifiers
  •  Naive Bayes Scratch Implementation using Python
  • Complement Naive Bayes (CNB) Algorithm
  • Applying Multinomial Naive Bayes to NLP Problems
• Support Vector
  • Support Vector Machine Algorithm
  • Support Vector Machines(SVMs) in Python
  • SVM Hyperparameter Tuning using GridSearchCV
  • Creating linear kernel SVM in Python
  • Major Kernel Functions in Support Vector Machine (SVM)
  • Using SVM to perform classification on a non-linear dataset
• Decision Tree
  • Decision Tree
  • Implementing Decision tree
  • Decision Tree Regression using sklearn
• Random Forest
  • Random Forest Regression in Python
  • Random Forest Classifier using Scikit-learn
  • Hyperparameters of Random Forest Classifier
  • Voting Classifier using Sklearn
  • Bagging classifier
• K-nearest neighbor (KNN)
  • K Nearest Neighbors with Python | ML
  • Implementation of K-Nearest Neighbors from Scratch using Python
  • K-nearest neighbor algorithm in Python
  • Implementation of KNN classifier using Sklearn
  • Imputation using the KNNimputer()
  • Implementation of KNN using OpenCV

Unsupervised Learning

• Types of Learning – Unsupervised Learning
• Clustering in Machine Learning
• Different Types of Clustering Algorithm
• K means Clustering – Introduction
• Elbow Method for optimal value of k in KMeans
• K-means++ Algorithm
• Analysis of test data using K-Means Clustering in Python
• Mini Batch K-means clustering algorithm
• Mean-Shift Clustering
• DBSCAN – Density based clustering
• Implementing DBSCAN algorithm using Sklearn
• Fuzzy Clustering
• Spectral Clustering
• OPTICS Clustering
• OPTICS Clustering Implementing using Sklearn
• Hierarchical clustering (Agglomerative and Divisive clustering)
• Implementing Agglomerative Clustering using Sklearn
• Gaussian Mixture Model

Deep Learning

• Introduction to Deep Learning
• Introduction to Artificial Neutral Networks
• Implementing Artificial Neural Network training process in Python
• A single neuron neural network in Python
• Convolutional Neural Networks
  • Introduction to Convolution Neural Network
  • Introduction to Pooling Layer
  • Introduction to Padding
  • Types of padding in convolution layer
  • Applying Convolutional Neural Network on mnist dataset
• Recurrent Neural Networks
  • Introduction to Recurrent Neural Network
  • Recurrent Neural Networks Explanation
  • seq2seq model
  • Introduction to Long Short Term Memory
  • Long Short Term Memory Networks Explanation
  • Gated Recurrent Unit Networks(GAN)
  • Text Generation using Gated Recurrent Unit Networks
• GANs – Generative Adversarial Network
  • Introduction to Generative Adversarial Network
  • Generative Adversarial Networks (GANs)
  • Use Cases of Generative Adversarial Networks
  • Building a Generative Adversarial Network using Keras
  • Modal Collapse in GANs

Natural Language Processing

• Introduction to Natural Language Processing
• Text Preprocessing in Python | Set – 1
• Text Preprocessing in Python | Set 2
• Removing stop words with NLTK in Python
• Tokenize text using NLTK in python
• How tokenizing text, sentence, words works
• Introduction to Stemming
• Stemming words with NLTK
• Lemmatization with NLTK
• Lemmatization with TextBlob
• How to get synonyms/antonyms from NLTK WordNet in Python?

How to Learn Data Science?

Usually, There are four areas to master data science.

1. Industry Knowledge : Domain knowledge in which you are going to work is necessary like If you want to be a data scientist in Blogging domain so you have much information about blogging sector like SEOs, Keywords and serializing. It will be beneficial in your data science journey.
2. Models and logics Knowledge: All machine learning systems are built on Models or algorithms, its important prerequisites to have a basic knowledge about models that are used in data science.
3. Computer and programming Knowledge : Not master level programming knowledge is required in data science but some basic like variables, constants, loops, conditional statements, input/output, functions.
4. Mathematics Used : It is an important part in data science. There is no such tutorial presents but you should have knowledge about the topics : mean, median, mode, variance, percentiles, distribution, probability, bayes theorem and statistical tests like hypothesis testing, Anova, chi squre, p-value.

Applications of Data Science

Data science is used in every domain.

• Healthcare : healthcare industries uses the data science to make instruments to detect and cure disease.
• Image Recognition : The popular application is identifying pattern in images and finds objects in image.
• Internet Search : To show best results for our searched query search engine use data science algorithms. Google deals with more than 20 petabytes of data per day. The reason google is a successful engine because it uses data science.
• Advertising : Data science algorithms are used in digital marketing which includes banners on various websites, billboard, posts etc. those marketing are done by data science. Data science helps to find correct user to show a particular banner or advertisement.
• Logistics : Logistics companies ensure faster delivery of your order so, these companies use the data science to find best route to deliver the order.

Career Opportunities in Data Science

• Data Scientist : The data scientist develops model like econometric and statistical for various problems like projection, classification, clustering, pattern analysis.
• Data Architect : The Data Scientist performs a important role in the improving of innovative strategies to understand the business’s consumer trends and management as well as ways to solve business problems, for instance, the optimization of product fulfilment and entire profit.
• Data Analytics : The data scientist supports the construction of the base of futuristic and various planned and continuing data analytics projects.
• Machine Learning Engineer : They built data funnels and deliver solutions for complex software.
• Data Engineer : Data engineers process the real-time gathered data or stored data and create and maintain data pipelines that create interconnected ecosystem within an company.

FAQs on Data Science Tutorial

Q.1 What is data science?

Answer:

Data science is an interconnected field that involves the use of statistical and computational methods to extract insightful information and knowledge from data. Data Science is simply the application of specific principles and analytic techniques to extract information from data used in planning, strategic , decision making, etc.

Q.2 What’s the difference between Data Science and Data Analytics ?

Answer:

Data Science	Data Analytics
Data Science is used in asking problems, modelling algorithms, building statistical models.	Data Analytics use data to extract meaningful insights and solves problem.
Machine Learning, Java, Hadoop Python, software development etc., are the tools of Data Science.	Data analytics tools include data modelling, data mining, database management and data analysis.
Data Science discovers new Questions.	Use the existing information to reveal the actionable data.
This domain uses algorithms and models to extract knowledge from unstructured data.	Check data from the given information using a specialised system.

Q.3 Is Python necessary for Data Science ?

Answer:

Python is easy to learn and most worldwide used programming language. Simplicity and versatility is the key feature of Python. There is R programming is also present for data science but due to simplicity and versatility of python, recommended language is python for Data Science.

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