+91 - 9327219987

info@bitbaroda.com

Data Science using Python Course

Embark on a transformative journey in data science with our Python course. Master essential skills in Python programming, data manipulation, and statistical analysis. Dive deep into machine learning algorithms and data visualization techniques. Our comprehensive curriculum ensures you gain expertise in data-driven decision-making. Join us to unlock endless opportunities in the dynamic field of data science.

|| What will I learn?

Understand the core concepts and techniques of data science.
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?

Understand the core concepts and techniques of data science.
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

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

Get in touch

I Accept The Terms & Conditions

I Agree To Get Program-related Info On WhatsApp & SMS

placement report

|| Frequently asked question

A Data Science using Python Course is designed to teach participants the essential concepts, techniques, and tools for performing data analysis, manipulation, visualization, and machine learning using the Python programming language and relevant libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.

This course is suitable for individuals interested in pursuing a career in data science, machine learning, or related fields. It caters to beginners with little to no programming experience as well as professionals looking to enhance their Python skills for data analysis and modeling.

Prerequisites may include a basic understanding of programming concepts, familiarity with Python syntax, and a willingness to learn data science concepts and techniques. Some courses may also recommend prior knowledge of mathematics and statistics.

Most reputable Data Science using Python courses offer a certificate of completion that can be shared on your resume or LinkedIn profile. However, it's essential to check the accreditation and recognition of the issuing institution before enrolling.

Some courses may offer job placement assistance or career services, including resume building, interview preparation, and networking opportunities. However, this varies depending on the course provider.

Yes, many Data Science using Python courses are available online, offering flexibility in terms of timing and location. Online courses often include video lectures, interactive coding exercises, and discussion forums to facilitate learning.

Some course providers offer financial aid or scholarships based on merit, financial need, or specific criteria. It's advisable to inquire with the course provider or check their website for information on available assistance programs.

After completing the course, you may continue to have access to resources such as course materials, coding exercises, alumni networks, coding communities, and additional learning resources. Some providers offer lifetime access to course materials or alumni benefits to support your continued growth and success.

Related courses

Full Stack Data Science Course

Data Structures & Algorithms Course

Machine Learning Course

Deep Learning Course

Artificial Intelligence Course

Computer Vision Course

Generative AI Course

Our latest blog

Visit our valuable articles to get more information.

Wed, 24 Apr 2024

Top 5 High Income skills to learn in 2024 to make a six – figure salary.

Top 5 High Income skills to learn in 2024 to make a six – figure salary.Seeking to propel your career this year and boost your earning potential? Sta...