Data Science using Python Course

|| Choose Data Science using Python Course From BIT

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|| What will I learn?

Core concepts like variables, data types, loops, and functions. Writing clean and efficient Python code.
Use Python and its libraries to collect, clean, and preprocess data.
Perform exploratory data analysis (EDA) and visualize data effectively.

Implement statistical analyses and hypothesis testing.
Build and evaluate machine learning models using Python.
Apply deep learning techniques for advanced data analysis.

|| What will I learn?

Core concepts like variables, data types, loops, and functions. Writing clean and efficient Python code.
Use Python and its libraries to collect, clean, and preprocess data.
Perform exploratory data analysis (EDA) and visualize data effectively.
Implement statistical analyses and hypothesis testing.
Build and evaluate machine learning models using Python.
Apply deep learning techniques for advanced data analysis.

|| Requirements

Basic programming knowledge (preferably in Python)

Familiarity with fundamental statistical concepts

|| Requirements

Basic programming knowledge (preferably in Python)
Familiarity with fundamental statistical concepts

|| Key Features of Comprehensive Data Science Using Python Program

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A comprehensive Data Science using Python course equips learners with the skills and knowledge necessary to analyse and interpret complex data, leveraging Python's powerful libraries and tools. The course typically begins with an introduction to Python programming, covering fundamental concepts such as data types, control structures, functions, and object-oriented programming. Following this, the curriculum delves into essential libraries for data science, including NumPy for numerical computations, Pandas for data manipulation and analysis, and Matplotlib and Seaborn for data visualization. The course then explores data wrangling techniques, teaching students how to clean, preprocess, and transform data to prepare it for analysis. This includes handling missing values, outliers, and performing feature engineering. Learners are also introduced to exploratory data analysis (EDA) methods, enabling them to uncover patterns, trends, and insights from datasets. Machine learning is a core component of the course, with modules dedicated to supervised and unsupervised learning algorithms. Students learn to implement and evaluate models using Scikit-Learn, covering regression, classification, clustering, and dimensionality reduction techniques. Advanced topics may include deep learning with TensorFlow or Keras, natural language processing (NLP), and time series analysis.

The curriculum emphasizes practical application through hands-on projects and case studies, where students work on real-world datasets to solve problems and derive actionable insights. Techniques for model selection, hyperparameter tuning, and model evaluation metrics are also covered to ensure robust and accurate predictions. In addition, the course often includes sections on big data technologies and tools such as Apache Spark for handling large datasets, as well as an introduction to databases and SQL for data retrieval and manipulation. Finally, students learn best practices for version control with Git and collaborative workflows, preparing them for professional data science roles. By the end of the course, learners are well-equipped with the technical skills and practical experience necessary to tackle data science challenges in various industries, making them valuable assets to any data-driven organization.

Python Basic Building
Python Keywords and identifiers
Comments, indentation, statements
Variables and data types in Python
Standard Input and Output

Operators
Control flow: if else elif
Control flow: while loop
Control flow: for loop
Control flow: break & continue
Python Data Structures

Strings
Lists, Lists comprehension
Tuples, Sets
Dictionary, Dictionary Comprehension

Python Functions
Python Builtin Functions.
Python Userdefined Functions.
Python Recursion Functions.
Python Lambda Functions.
Python Exception Handling,
Logging And Debugging

Exception Handling
Custom Exception Handling
Logging With Python
Debugging With Python
Python OOPS
Python Objects And Classes
Python Constructors
Python Inheritance
Abstraction In Python
Polymorphism in Python
Encapsulation in Python

File Handling
Create
Read
Write
Append

Introduction to NumPy
NumPy Array
Creating NumPy Array
Array Attributes,
Array Methods
Array Indexing,
Slicing Arrays
Array Operation
Iteration through Arrays

Introduction to Pandas
Pandas Series
Creating Pandas Series
Accessing Series Elements
Filtering a Series
Arithmetic Operations
Series Ranking and Sorting
Checking Null Values
Concatenate a Series

Data Frame Manipulation
Pandas Dataframe
Introduction Dataframe Creation
Reading Data from Various Files
Understanding Data
Accessing Data Frame Elements using Indexing
Dataframe Sorting
Ranking in Dataframe
Dataframe Concatenation
Dataframe Joins
Dataframe Merge
Reshaping Dataframe
Pivot Tables,
Cross Tables
Dataframe Operations

Checking Duplicates
Dropping Rows and Columns
Replacing Values
Grouping Dataframe
Missing Value Analysis & Treatment
Visualization using Matplotlib
Plot Styles & Settings
Line Plot,
Multiline Plot
Matplotlib Subplots
Histogram, Boxplot
Pie Chart ,Scatter Plot
Visualization using Seaborn
Strip Plot ,Distribution Plot
Joint Plot,
Violin Plot,
Swarm Plot
Pair Plot,
Count Plot
Heatmap
Visualization using Plotly
Boxplot
Bubble Chart
Violin Plot
3D Visualization

EDA and Feature Engineering
Introduction of EDA
Dataframe Analysis using Groupby
Advanced Data Explorations

Working with SQL Using MySQL
Work Bench / SQL Server"
USE, DESCRIBE,
SHOW TABLES
SELECT, INSERT
UPDATE & DELETE
CREATE TABLE
ALTER: ADD, MODIFY, DROP
DROP TABLE, TRUNCATE, DELETE
LIMIT, OFFSET
ORDER BY
DISTINCT
WHERE Clause
HAVING Clause
Logical Operators
Aggregate Functions: COUNT, MIN, MAX, AVG, SUM
GROUP BY
SQL Primary And Foreign Key
Join and Natural Join
Inner, Left, Right and Outer joins

Advance SQL
Subqueries/Nested Queries/Inner Queries
SQL Function And Stored Procedures
SQL Window Function
CTE In SQL
Normalization In SQL

Basic Math
Linear Algebra
Probability
Calculus
Develop a comprehensive understanding of coordinate geometry and linear algebra.
Build a strong foundation in calculus, including limits, derivatives, and integrals.

Descriptive Statistics
Sampling Techniques
Measure of Central Tendency
Measure of Dispersion
Skewness and Kurtosis
Random Variables
Bassells Correction Method
Percentiles and Quartiles
Five Number Summary
Gaussian Distribution
Lognormal Distribution
Binomial Distribution
Bernoulli Distribution

Inferential Statistics
Standard Normal Distribution
ZTest
TTest
ChiSquare Test
ANOVA / FTest
Introduction to Hypothesis Testing
Null Hypothesis
Alternet Hypothesis

Probability Theory
What is Probability?
Events and Types of Events
Sets in Probability
Probability Basics using Python
Conditional Probability
Expectation and Variance

Introduction to Machine Learning
Machine Learning Modelling Flow
"Supervised and Unsupervised
Types of Machine Learning Algorithms

Linear Regression using OLS
Introduction of Linear Regression
Types of Linear Regression
OLS Model
Math behind Linear Regression
Decomposition Variability
Metrics to Evaluate Model
Feature Scaling
Feature Selection
Regularisation Techniques
Ridge Regression
Lasso Regression
ElastivNet Regression

Optimisation Techniques
What is Optimisation?
Gradient Descent
Adagrad Algorithm
Adam Algorithm
Linear Regression with SGD
Prerequisites

Introduction to Stochastic Gradient Descent (SGD)
Preparation for SGD
Workflow of SGD
Implementation of SGD on Linear Regression

Logistic Regression
Maximum Likelihood Estimation
"Logistic Regression Using Sigmoid
Activation Function"
Performance Metrics
Confusion Matrix
Precision, Recall, F1Score
Receiver Operating Characteristic Curve

KNN
Euclidean Distance
Manhattan Distance
Implementation for KNN

SVM
Support Vector Regression
Support Vector Classification
Polynomial Kernel
Cost Function
GridSerchCV

Decision Trees
Decision Tree for Classification
Decision Tree for Regression
ID3 Algorithm
CART Algorithm
Entropy
Gini Index
Information Gain
Decision Tree: Regression
Mean Square Error
PrePruning and PostPruning

Naive Bayes
Introduction to Bayes Theorem
Explanation for naive bayes

Ensemble Technique
Bagging
Random Forest Classifier
Random Forest Regression
Random Forest – Why & How?
Feature Importance
Advantages & Disadvantages

Boosting
Bootstrap Aggregating
AdaBoost
XgBoost
Project For Random Forest
Project Penguin Classification
Project Texi Prediction

Kmeans Clustering
Prerequisites
Cluster Analysis
Kmeans
Implementation of Kmeans
Pros and Cons of Kmeans
Application of Kmeans
Elbow Method
Model building for Kmeans Clustering

Hierarchical Clustering
Types of Hierarchical Clustering
Dendrogram
Pros and Cons of Hierarchical Clustering
Model building for Hierarchical Clustering

DBSCAN Clustering
Introduction for DBSCAN Clustering
implementation of DBSCAN

Principal Components Analysis
Prerequisites
Introduction to PCA
Principal Component
Implementation of PCA
Case study
Applications of PCA
Project on PCA

Time Series Modelling
Understand Time Series Data
Visualising Time Series Components
Exponential Smoothing
ARIMA
SARIMA
SARIMAX
Project on Forecasting
Cloud Basics
ML on Cloud

Developing a Data Science project using Python involves several stages, from problem identification to model deployment. Here is a comprehensive guide outlining the process:

Problem Definition:
Objective: Clearly define the problem you aim to solve or the question you seek to answer.
Scope: Determine the scope of the project, including the expected outcomes and success criteria.

Data Collection:
Sources: Identify and gather relevant data from various sources such as databases, APIs, web scraping, or publicly available datasets.
Quality: Ensure the data collected is reliable, accurate, and sufficient for analysis.

Data Cleaning:
Handling Missing Values: Deal with missing data through imputation or removal.
Data Types: Correct data types for each feature (e.g., integers, floats, categories).
Outliers: Identify and handle outliers that may skew the results.
Normalization/Standardization: Scale the data if necessary, especially for algorithms sensitive to the magnitude of data.

Exploratory Data Analysis (EDA):
Visualization: Use plots (histograms, scatter plots, box plots) to understand data distribution and relationships.
Summary Statistics: Compute mean, median, mode, standard deviation, and other relevant statistics.
Insights: Identify patterns, trends, and anomalies within the data.

Feature Engineering:
Creation: Develop new features from existing data that can improve model performance.
Transformation: Apply transformations (logarithmic, polynomial) to features to meet model assumptions.
Selection: Select the most relevant features using techniques like correlation analysis, and feature importance scores.

Model Selection:
Algorithm Choice: Choose appropriate algorithms based on the problem type (e.g., regression, classification, clustering).
Libraries: Utilize Python libraries such as Scikit-learn, TensorFlow, Keras, or PyTorch for model implementation.

Model Training:
Splitting Data: Divide data into training and testing sets, and possibly a validation set.
Training: Fit the model to the training data.
Hyperparameter Tuning: Optimize model parameters using techniques such as grid search or random search.

Model Evaluation:
Metrics: Use relevant evaluation metrics (e.g., accuracy, precision, recall, F1 score, RMSE) to assess model performance.
Validation: Validate the model using cross-validation or a separate validation set to ensure generalizability.
Model Deployment:
Implementation: Deploy the model into a production environment using tools like Flask, Django, or FastAPI.
Integration: Integrate the model with existing systems or applications.
Monitoring: Set up monitoring to track the model’s performance over time and detect any degradation.

Documentation and Reporting:
Documentation: Document the entire project, including the data sources, methodologies, models used, and the results obtained.
Reporting: Prepare comprehensive reports or dashboards to communicate findings and insights to stakeholders.

Maintenance and Updates:
Retraining: Periodically retrain the model with new data to maintain accuracy and relevance.
Improvements: Continuously improve the model based on feedback and performance monitoring.

By following this structured process, you can develop robust and reliable data science projects using Python that provide valuable insights and solve complex problems effectively.

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|| Become An Expert In Data Science with Python

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|| Why a Career in Data Science Using Python is a Good Option in India

High Demand for Data Scientists:
The demand for data scientists in India is growing rapidly as more companies recognize the value of data-driven decision-making.
Industries such as finance, healthcare, e-commerce, and technology are heavily investing in data science capabilities, leading to a surge in job opportunities.

Python as a Preferred Language:
Python is widly used in data science due to its simplicity, readability, and extensive library support.
Libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch make Python an ideal choice for data analysis, machine learning, and deep learning tasks.

Attractive Salary Packages:
Data scientists in India enjoy competitive salaries that are significantly higher than many other professions.
As per industry reports, experienced data scientists can earn lucrative packages, making it a financially rewarding career.

Diverse Career Opportunities:
A career in data science offers a wide range of roles including Data Analyst, Machine Learning Engineer, Data Engineer, Business Analyst, and Research Scientist.
The versatility of the field allows professionals to work in various domains and industries.

Contribution to Business Success:
Data scientists play a crucial role in helping businesses make informed decisions by analyzing trends, predicting outcomes, and providing actionable insights.
Their work directly impacts business strategies, efficiency, and profitability.

Growing Ecosystem:
India has a rapidly growing ecosystem of startups and established companies that are investing heavily in data science and AI.
This creates a vibrant job market with ample opportunities for growth and innovation.

Continuous Learning and Development:
The field of data science is dynamic, offering continuous learning opportunities through new tools, techniques, and research.
Professionals can keep their skills updated and stay relevant in the industry.

Supportive Community and Resources:
There is a strong community of data scientists and Python developers in India who actively share knowledge and support each other through forums, conferences, and meetups.
Numerous online resources, courses, and certifications are available to help aspiring data scientists build and enhance their skills.

Government and Industry Initiatives:
The Indian government and various industry bodies are promoting data science and AI through initiatives, policies, and funding.
This support boosts the growth of the field and creates more opportunities for professionals.

Global Opportunities:
Proficiency in data science and Python opens up global career opportunities, allowing professionals to work with international companies or even relocate abroad for better prospects.

In summary, a career in data science using Python in India is a promising option due to the high demand, attractive salaries, diverse opportunities, and the ability to make significant contributions to business success. The continuous evolution of the field and the supportive ecosystem further enhance its appeal.

|| Career & Opportunities after Data Science with Python

Here are some prominent career options and opportunities:

Data Scientist
Role: Analysing large datasets to extract actionable insights, building predictive models, and helping organizations make data driven decisions.
Opportunities: Available across sectors such as finance, healthcare, ecommerce, technology, and more. Companies like IBM, Microsoft, and local startups are constantly seeking skilled data scientists.
Data Analyst
Role: Interpreting data, generating reports, and visualizing data to help organizations understand trends and patterns.
Opportunities: Every industry requires data analysts, from retail and marketing to finance and government.
Machine Learning Engineer
Role: Designing and implementing machine learning models, working on deep learning, natural language processing (NLP), and computer vision projects.
Opportunities: Tech companies like Google, Facebook, and Amazon, as well as AI focused startups, are major employers.
Business Intelligence (BI) Developer
Role: Developing strategies to assist business decisions, creating dashboards, and providing insights from business data.
Opportunities: Companies in finance, marketing, sales, and other business domains require BI developers to leverage data for strategic planning.
Data Engineer
Role: Building and maintaining data pipelines, ensuring data quality, and supporting data infrastructure.
Opportunities: Essential in companies dealing with big data, such as tech firms, financial institutions, and telecommunication companies.
Data Architect
Role: Designing and managing the data architecture of an organization, ensuring data integration and security.
Opportunities: Large enterprises and cloud-based companies often need data architects to handle complex data systems.
Research Scientist
Role: Conducting advanced research in data science, developing new algorithms and models.
Opportunities: Academic institutions, research labs, and companies like IBM Research and Google Research offer roles for research scientists.
Big Data Specialist
Role: Handling, processing, and analysing large datasets using big data technologies like Hadoop and Spark.
Opportunities: Companies dealing with massive data, such as financial services, healthcare, and internet services, often hire big data specialists.

|| Data Science with Python Scope in India

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|| Companies Using Data Science with Python

Google
Uses Python for data analysis, machine learning models, and AI projects. TensorFlow, a popular machine learning framework developed by Google, supports Python.
Facebook (Meta)
Utilizes Python for various data science tasks, including user behaviour analysis, ad targeting, and recommendation systems.
Amazon
Employs Python for data-driven decisions, customer behaviour analysis, and optimizing logistics and supply chain operations.
Microsoft
Uses Python for Azure Machine Learning services, data analysis, and AI research. Python is integrated with many Microsoft products and services.
Apple
Utilizes Python for machine learning, data analysis, and various AI-driven features in its products and services.
JPMorgan Chase
Uses Python for quantitative analysis, risk management, and trading algorithms.
Goldman Sachs
Employs Python for data analysis, financial modelling, and machine learning applications in finance.
American Express
Uses Python for fraud detection, customer analytics, and improving customer experiences.
eBay
Utilizes Python for data analysis, recommendation systems, and personalized user experiences.
Alibaba
Employs Python for customer behaviour analysis, inventory management, and optimizing logistics.
Pfizer
Uses Python for data analysis in clinical trials, drug discovery, and patient data management.
Roche
Employs Python for genomic data analysis, personalized medicine, and clinical research.
Walmart
Uses Python for demand forecasting, inventory optimization, and customer behaviour analysis.
Target
Utilizes Python for data-driven marketing, supply chain optimization, and sales analysis.
Verizon
Employs Python for network optimization, customer data analysis, and predictive maintenance.
AT&T
Uses Python for data analytics, improving customer services, and network management.
Netflix
Uses Python for recommendation systems, content personalization, and data-driven decision-making.
Spotify
Employs Python for music recommendation algorithms, user behaviour analysis, and improving user experiences.
Airbnb
Utilizes Python for pricing algorithms, recommendation systems, and data analysis.
Uber
Uses Python for route optimization, demand forecasting, and dynamic pricing models.
Lyft
Employs Python for data-driven decision-making, ride optimization, and user behaviour analysis.
IBM
Uses Python for data analytics, machine learning solutions, and AI research.
Tesla
Utilizes Python for autonomous driving algorithms, energy management, and data analysis.

These companies use Python in their data science workflows due to its ease of use, powerful libraries, and strong community support. The language's flexibility allows for rapid prototyping and implementation of complex data-driven solutions, making it a preferred choice across industries.

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|| Salary after Data Science with Python in India

Here is a general overview of the expected salary ranges for data science professionals with Python skills at different career stages:

Fresher (0-2 years of experience)
Salary Range: ₹5,85,000 per annum
Typical Roles: Junior Data Scientist, Data Analyst, Machine Learning Intern
Influencing Factors: Academic background, internship experience, skill proficiency, location, and company reputation.
Intermediate (2-5 years of experience)
Salary Range: ₹13,00,000 per annum
Typical Roles: Data Scientist, Machine Learning Engineer, Senior Data Analyst
Influencing Factors: Level of expertise, project experience, certifications, advanced skills in machine learning and data engineering, location, and company size.
Experienced (7+ years of experience)
Salary Range: ₹20,40,000 + per annum
Typical Roles: Senior Data Scientist, Lead Data Scientist, Data Science Manager, AI Specialist
Influencing Factors: Leadership experience, domain expertise, contributions to significant projects, published research, location, and the scale of the company’s operations.
Influencing Factors Across All Levels
Location: Salaries can vary significantly depending on the city. Major tech hubs like Bengaluru, Mumbai, Hyderabad, and Gurgaon typically offer higher salaries compared to other cities.
Company Type: Large multinational companies and well-funded startups often offer higher salaries compared to smaller companies or those in less lucrative industries.
Industry: Finance, technology, e-commerce, and healthcare sectors tend to offer higher compensation for data science roles.
Additional Benefits

Many companies also provide additional benefits such as performance bonuses, stock options, health insurance, and opportunities for professional development, which can significantly enhance the overall compensation package.

The demand for skilled data science professionals in India is high, and with the right skills and experience, individuals can expect lucrative career opportunities. Continuous learning and staying updated with the latest trends in data science and Python can further enhance career prospects and salary potential.

|| Job Roles & Salary

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|| Data Science with Python offers abundant placement opportunities in India

Data Science with Python offers abundant placement opportunities in India due to the increasing demand for skilled data scientists and analysts across various industries. With the country's growing emphasis on digital transformation and data-driven decision-making, organizations in sectors such as IT, finance, healthcare, e-commerce, and telecommunications are actively seeking professionals proficient in Python for data science. Data scientists proficient in Python are sought after for their ability to manipulate and analyze large datasets efficiently, build predictive models, and derive actionable insights to drive business growth and innovation. Furthermore, India's thriving startup ecosystem and the emergence of data-centric companies have created additional avenues for data science professionals to contribute their expertise. With competitive salaries, attractive perks, and opportunities for career advancement, data science roles in India offer promising prospects for individuals with skills in Python and a strong foundation in data analysis and machine learning techniques. As the demand for data-driven solutions continues to rise, the placement opportunities for Data Science with Python professionals in India are expected to expand further, making it an attractive career path for aspiring data enthusiasts.

|| Empowering Your Career Transition From Learning To Leading

Megha Bhatt

Megha Bhatt, an ML Engineer at Cognizant, demonstrates prowess by leveraging unique tools such as Alteryx for advanced data blending and Google BigQuery for large-scale data analytics. Her adept use of these cutting-edge tools contributes to innovative and efficient data analysis.

Darshna Dave

Darshna Dave, excelling as a Data Analyst at Deepak Foundation post-IT institute, showcases expertise in unique tools such as KNIME for data analytics workflows, Apache Superset for interactive data visualization, and RapidMiner for advanced predictive analytics.

Shubham Ambike

Shubham Ambike, excelling as a Digital MIS Executive at Alois post-Business Analytics course, showcases expertise in tools like Microsoft Excel, Power BI, and Google Analytics. His adept use of these tools contributes to efficient data management and analysis. Congratulations on his placement.

Mehul Sirohi

Mehul Sirohi, excelling as a Data Associate at Numerator post learning Data Analytics course, skillfully employs unique tools such as Alteryx for data blending, Jupyter Notebooks for interactive data analysis, and Looker for intuitive data visualization. His mastery of these advanced tools contributes to Numerator's data processing success.

Pratik Shah

Pratik Shah excels in Data Processing at NielsenIQ after studying a Full-Stack Data science course from BIT. Proficient in tools like Excel, SQL, and Python, Pratik ensures precise and efficient data handling. Congratulations on his placement, which showcases his expertise in essential data processing tools.

Vanshika Patel

Vanshika Patel, demonstrating mastery as a Sr. ML Engineer at Genpact, demonstrates proficiency in essential tools such as Python, TensorFlow, and scikit-learn. Her adept use of these critical software contributes to effective machine learning solutions.

Pratik Shah

Pratik Shah excels in Data Processing at NielsenIQ post our IT institute. Proficient in tools like Excel, SQL, and Python, Pratik ensures precise and efficient data handling. Congratulations on his placement, showcasing expertise in essential data processing tools.

|| Get Data science using Python Certification

Three easy steps will unlock your Data science using Python Certification:

Finish the online / offline course of Data science using Python Course and the Assignment
Take on and successfully complete a number of industry-based Projects
Pass the Data science using Python certification exam

The certificate for this Data science using Python course will be sent to you through our learning management system, where you can also download it. Add a link to your certificate to your CV or LinkedIn profile.

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|| Choose Data Science using Python Course From BIT

|| What will I learn?

|| What will I learn?

|| Requirements

|| Requirements

|| Key Features of Comprehensive Data Science Using Python Program

DATA SCIENCE WITH PYTHON COURSE CONTENT

Python Programming

Advance Python Programming

Relational Database (SQL)

Mathematics

Basic & Advance Statistic

Machine Learning

Project development

Get in touch

|| Become An Expert In Data Science with Python

|| Why a Career in Data Science Using Python is a Good Option in India

|| Career & Opportunities after Data Science with Python

|| Data Science with Python Scope in India

|| Companies Using Data Science with Python

|| Top Hiring Companies

|| Salary after Data Science with Python in India

|| Job Roles & Salary

|| Data Science with Python offers abundant placement opportunities in India

|| Empowering Your Career Transition From Learning To Leading

Megha Bhatt

Darshna Dave

Shubham Ambike

Mehul Sirohi

Pratik Shah

Vanshika Patel

Pratik Shah

|| Get Data science using Python Certification

|| Frequently asked question

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