Data Science With Python Training

4.80

(3214)

DURATION

3 Months

EMI

0% Interest

Mode

Live Online / Offline

Softlogic is one of the Top Training institutes for Data Science with Python. Learn how to use Data Science with Python, from beginner basics to advanced techniques. Our Data Science with Python Syllabus covers the Python fundamentals, data manipulation with Pandas, numerical computing with NumPy, data visualization with Matplotlib and Seaborn, statistical analysis, and implementing machine learning models with scikit-learn. We offer a Data Science with Python Course with Placement Assistance, Mock interviews, Resume building, and certification.

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Syllabus of Data Science With Python Course

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Python – Overview

A brief history of python
Application and trends in python
Available python versions

Python – Environment Setup

Getting and installing python
Environmental variables and idle
Executing python from command line

Fundamentals

I/o
Naming conventions
Datatypes:
Numbers
String
List
Tuple
Dictionary
Set

Python Operators

List, Tuple, Dictionary, Set Methods
Statements: If, elif, Break, Continue
Loops: For loop, while loop
Functions

Oops Concepts:

Class and objects
Getters and setters
Properties
Inheritance
Polymorphism
Special Functions of Python: Lambda, Map, Reduce, Filter

Modules in Python:

Math
Arrow
Geopy
Beautiful soup
Numpy
Sys
Os

Multithreading:

Introducing threads and life cycles
Priorities
Dead Locks

Exceptional Handling

Errors
Runtime errors
Exceptional model
Exceptional hierarchy
Handling multiple exception
Raise exceptions

File Handling

Text files
Csv files

Regular Expressions

Simple character matches
Flags, quantifers, greedy matches
Grouping and matching objects
Matching at beginning or end
Substituting and splitting a string
Compiling regular expressions

Gui Interfacing: Tkinter

Widgets
Integrated application
Mysql/with application
Converting .exe

Datascience Modules:

pandas
numpy
scipy
matplotlib

Python Data Processing

Python Data Operations
Python Data cleansing
Python Processing CSV Data
Python Processing JSON Data
Python Processing XLS Data
Python Relational databases
Python NoSQL Databases
Python Date and Time
Python Data Wrangling
Python Data Aggregation
Python Reading HTML Pages
Python Processing Unstructured Data
Python word tokenization
Python Stemming and Lemmatization

Python Data Visualization

Python Chart Properties
Python Chart Styling
Python Box Plots
Python Heat Maps
Python Scatter Plots
Python Bubble Charts
Python 3D Charts
Python Time Series
Python Geographical Data
Python Graph Data

Statistical Data Analysis

Python Measuring Central Tendency
Python Measuring Variance
Python Normal Distribution
Python Binomial Distribution
Python Poisson Distribution
Python Bernoulli Distribution
Python P-Value
Python Correlation
Python Chi-square Test
Python Linear Regression

Objectives of Data Science With Python Course

The Data Science with Python Training will cover all the topics ranging from fundamental to advanced concepts, which will make it easy for students to grasp Data Science with Python. The Data Science with Python Course Curriculum is composed of some of the most useful and rare concepts that will surely give students a complete understanding of Data Science with Python as well. So, some of those curriculum are discussed below as objectives:

To make students well-versed in fundamental concepts of Data Science with Python like – Brief History of Python, Python Environment Set Up, Python Operators, OOPs concepts, Modules in Python etc.
To make students more aware of Data Science with Python by learning concepts like – Exception Handling, File Handling, Regular Expressions, Tkinter etc.
To make students more knowledgeable in advanced concepts of Data Science with Python like – Python Data Processing, Python Data Visualization, Statistical Data Analysis – Python Measuring Central Tendency, Python Measuring Variance, Python Normal Distribution etc.

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Highlights of Data Science With Python Course?

What is Data Science with Python?

Data Science with Python involves leveraging Python for analyzing data, building predictive models, and extracting insights. Key areas include data collection and cleaning with Pandas, exploratory analysis with Matplotlib and Seaborn, machine learning with Scikit-learn, and deploying models with Flask. Python’s libraries facilitate comprehensive data manipulation and visualization.

What is Data Science with Python Full Stack?

Data Science with Python Full Stack encompasses every phase of a data science project using Python, including data acquisition, cleaning, and transformation. It involves exploratory analysis with tools like Matplotlib and Seaborn, machine learning with Scikit-learn and TensorFlow, and deployment through frameworks like Flask or Django, with ongoing monitoring and updates.

What are the reasons for learning Data Science with Python?

The following are the reasons for learning Data Science with Python:

Flexibility: Python is adaptable and extensively used in various aspects of data science, including data manipulation, analysis, and machine learning.

Rich Libraries: Python offers a range of powerful libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and Matplotlib, which streamline data science processes and simplify complex tasks.

User-Friendly: The clear syntax and readability of Python make it approachable for both newcomers and seasoned data scientists, promoting faster learning and implementation.

Strong Community: An active and supportive community offers a wealth of resources, tutorials, and assistance, improving the learning experience and problem-solving.

What are the prerequisites for learning Data Science with Python?

The following are the prerequisites for learning Data Science with Python:

Libraries: Familiarity with essential libraries such as Pandas for data manipulation, NumPy for numerical calculations, and Matplotlib for basic plotting.
Statistics: Grasp of probability, statistical distributions, hypothesis testing, and regression techniques.
Data Manipulation: Proficiency with Pandas for reshaping, aggregating, and transforming datasets.
Model Evaluation: Understanding of metrics like accuracy, precision, recall, and F1 score to evaluate model performance.

What are the course fees and duration?

Our Data Science with Python Course Fees may vary depending on the specific course program you choose (basic / intermediate / full stack), course duration, and course format (remote or in-person). On an average the Data Science with Python Course Fees range from 40k to 50k, for a duration of 4 months with international certification based on the above factors.

What are some of the jobs related to Data Science with Python?

The following are the jobs related to Data Science with Python:

Data Scientist
Data Analyst
Business Intelligence (BI) Analyst
Machine Learning Engineer
Data Engineer
Quantitative Analyst
Data Science Consultant

List a few real time Data Science with Python applications.

The following are the real-time Data Science with Python applications:

Real-Time Fraud Detection
Predictive Maintenance
Real-Time Traffic Management
Personalized Recommendation
Social Media Sentiment Analysis
Dynamic Pricing Models

Data Science With Python Course
Interview Questions and Answers

Boost your skills with Data Science With Python Course with Experts

Our Mentors are from Top Companies like:

Our trainers possess over 10 years of extensive experience in Data Science and Python programming.
They have a deep understanding of application development, web programming, and the creation of analytical algorithms.
They offer insightful methods for developing predictive data models and implementing industry best practices.
They have led numerous workshops that explore the complexities of Data Science and provide a thorough overview of relevant software.
They utilize their expertise to empower students with advanced data processing techniques and help address common challenges in data analysis, visualization, predictive modeling, and programming.
They share their knowledge on problem-solving techniques using Python libraries and offer hands-on experience across various scientific disciplines.
They excel at customizing the course curriculum to meet the specific needs of each student and providing constructive feedback to enhance learning outcomes.
They ensure that students achieve a comprehensive understanding of the subject matter and the content covered in each session.
They deliver robust support throughout the training program, ensuring an exceptional learning experience.
Leveraging their experience and skills, they are dedicated to helping students meet their training goals and become proficient in Data Science and Python programming.

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Explore Real Projects Built by Softlogic Students

Explore the real-world, enterprise-level projects designed and executed by our successful alumni. Gain the same hands-on exposure to live industry projects, build a standout portfolio, and prove your practical skills to top hiring managers instantly.

Healthcare Predictive Analytics

Predictive Maintenance

Real-Time Traffic Analysis

Project Practices for Data Science With Python Course

Why Softlogic Systems is the Best Choice for Data Science With Python Course

Online & Offline Training Options

Learn at your convenience with flexible classroom and live online training.

Learn from 100+ Real-Time Developers

Get trained by industry professionals with years of hands-on experience.

Hands-on Projects & Codeathons

Practice with real-time projects and coding challenges to build confidence.

0% EMI Fee Options

Pay your course fees flexibly with easy EMI plans at zero interest.

Resume & Interview Support

Get expert help with resume building, mock interviews, and soft skills.

Placement with Top IT Firms

Access placement opportunities with leading MNCs and IT companies.

500+ Hiring Partners

Benefit from Softlogic’s strong recruiter network for faster job placement.

No Backdoor Jobs

We ensure only genuine placement opportunities with trusted companies.

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Certifications

Earn Your Certificate of Completion

Validate your achievements with Softlogic's Certificate of Completion, verifying successful fulfillment of all essential components.

Take Your Career to the Next Level with Certifications

Get a certifications through our training programs to gain a competitive edge in the industry.

Stand Out from the Crowd with Codethon Certificate

Verify the authenticity of your real-time projects with Softlogic's Codethon certificate.

The Global Edge: Certified Vs. Uncertified

With Global Certification

✅ Visibility in ATS Filters
✅Command Higher Hikes
✅ Verified Skill Proof
✅Global Job Access
✅ Fast-Tracked Senior Roles

Without Global Certification

❌ Stuck in Screening
❌ Lower Starting Pay
❌ Unproven Technical Ability
❌ Limited Local Scope
❌ Slower Career Leader

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The SLA Way to Get Placed

Seven connected stages, one continuous path from enrollment to your career launch — here is exactly how it works, step by step.

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Technical Mastery

Industry-aligned curriculum, daily coding tasks, real-time projects

💻

Career Readiness

Aptitude, Communication, ATS-friendly resume optimization

🎯

Placement Prep

Aptitude, communication tuning and technical interview mastery.

💬

Panel Mock Interviews

Real company-pattern mocks with expert panel feedback.

👨‍💼

Interview schedule

Multiple company drives with lifetime placement support.

📝

Interview Feedback

Detailed performance analysis and one-on-one guidance.

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Launch Your Career

Land your first job with lifetime alumni and career support.

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FAQ

What are the benefits of using Python for data science compared to other languages?

Python is highly regarded for data science due to its clear syntax and readability, a rich set of libraries (such as Pandas, NumPy, and Scikit-learn), robust community support, and seamless integration capabilities. These aspects make it an effective tool for data manipulation, analysis, and modeling.

What distinguishes Pandas from NumPy in the context of data science?

Pandas is designed for data manipulation and analysis, providing DataFrames for data handling and cleaning. In contrast, NumPy is focused on numerical operations, offering support for arrays and mathematical functions necessary for performing efficient computations.

What are some standard data preprocessing methods applied in Python before using machine learning models?

Key preprocessing methods include data cleaning (addressing missing values and outliers), normalization (scaling features to a consistent range), encoding categorical variables, as well as feature extraction and selection. These steps are essential for optimizing model performance and accuracy.

What strategies can be used to handle missing data in a Python-based data science project?

Missing data can be managed through imputation techniques (such as filling in missing values with mean, median, or mode), removal of missing values, or employing algorithms capable of handling them. Pandas provides functions like fillna() and dropna() for these tasks.

What is the purpose of Jupyter Notebooks in Python data science projects?

Jupyter Notebooks offer an interactive platform for writing and running Python code, visualizing data, and documenting the analysis process within a single document. This integration facilitates effective exploration, analysis, and presentation of data.

How does the Scikit-learn library support machine learning in Python?

Scikit-learn provides a comprehensive set of tools for developing and evaluating machine learning models. It includes implementations for various algorithms used in classification, regression, clustering, and dimensionality reduction, along with utilities for model assessment and selection.

Why is version control important in data science projects, and how does it benefit Python projects?

Version control is crucial for tracking code changes, managing project progress, and facilitating collaboration. For Python projects, tools like Git help data scientists maintain code versions, monitor changes, and collaborate more efficiently, minimizing errors and improving workflow.

In what ways can Python be utilized for real-time data analysis and processing?

Python supports real-time data analysis through libraries and tools designed for data streaming (e.g., Apache Kafka, Streamlit) and asynchronous processing. Additionally, frameworks like Flask or Django and libraries such as asyncio can be employed to develop applications for real-time data processing and analysis.

Where is the corporate office of Softlogic Systems located?

The corporate office of the Softlogic Systems is located at the institute’s K.K.Nagar branch.

What payment methods does Softlogic accept?

Softlogic accepts a wide range of payment methods, including:

Cash
Debit cards
Credit cards (MasterCard, Visa, Maestro)
Net banking
UPI
Including EMI.

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