Best Data Science Course In Solapur With Placement
100% Placement Assistance | Live Online Sessions
Data Science course : 4 Month Personalized Live Advance Data Science Training is taught by industry experts in a comprehensive & question-oriented format.
Enroll Before: 07 December, 2024
1000+
Students Trained
100%
Placement Assistance
07 December, 2024
Start Date
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EMI Available
7:30 AM - 9:30 AM
Lecture Timings ( IST )
Key Highlights Of The Advance Data Science Course
- 100+ Hours Of Live Class
- 50+ Hours of Videos
- 1-on-1 Mentoring Sessions
- 20+ Industry Tools Mastery
- In-Class Live Presentations
- 3+ Live Projects & Practice Assignments
- 100% Placement Assistance
- Resume & Interview Training
Syllabus for Data Science Course
Online Advance Data Science Course in Solapur is designed to teach students the basics to the advanced level concepts of Python Data Science with practice assignments and offline in-class projects which helps them to get placed in MNC’s.
Term 1
In this term, you will learn how to ace Python Basics, Python OOPS and Python Libraries like Pandas, Matplotlib, Numpy, etc…Â Â
Python Basics :
- Â Why python
- Python IDE
- Basics of programming
- Variables , Data Types
- Conditional statements
- Loops
- Logical Thinking
- Data Structures
- Functions and types of arguments
- Lambda Functions
- memory Management
- garbage collector
- Copies - shallow copy, deep copy
- Higher Order Functions - Map , Reduce , Filter
- Iterable , Iterator , generator
- Exception handling
- Programming interview questions
Python OOPS :
- Class
- constructor and its types , Destructor
- Types of variables - instance , static
- Inheritance - Single , Multiple , Multilevel , Hierarchical
- polymorphism
- duck typing
- Overloading - method , Operator , constructor
- overriding - method , Constructor
- Super Function
- Encapsulation
- access Modifiers
- Abstraction
- monkey patching
Pandas :
- Introduction to Pandas
Series Data Structure - Data Frame Data Structure
- Merging DataFrame
- Read Complex CSV , JSON , excel Files using pandas
- Write to File
- Data Frame Manipulation - head , Tail , Describe , shape ,Drop , inplace
- loc & I=iloc
- Apply Function
- Value count
- Add Column
- Add Row To DataFrame - using concat,Append
- Order By Operation
- Sort Values
- Group by operation
- Pivot Table
- Date/ Time Functionality
- Example Manipulating DataFrame
Matplotlib :
- line graph
- bar Plot
- scatter plot
- pie chart
- other function
Numpy :
- Introduction to Numpy
- Creating Arrays , Indexing , Slicing
- Data Types
- Copy vs View
- Array Shape & Reshape
- Arrays Split & Joins
- Arrays Filter
- Seaborn Model
Data Engineering in Python :
- Handling Missing Data
- Techniques to inpute missing Values
- Meaningful Data transformation
- Encoding Data
Data Visualization in Python - Read Json , CSV's, excels
Term 2
In this term, you will learn how to ace MySQL, AWS, Tools & IDE’S
MYSQL :
- DBMS & RDBMS
- Data Types
- DQL
- DDL
- DML
- TCL
- DCL
- Key Constraints
- Operators
- Clouses
- Aggregate Functions
- Indexes
- Views
- Triggers
- JOINS
- Sub Queries & Nested Queries
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- Use of AWS
- Cloud computing models
- S3
- AWS Data Pipeline
- EMR
- AWS Glue
- Athena
- Redshift
Azure devops :
- Use Of Devops
- CI/ CD Pipeline
- work item
- sprints
- repository
- state of task
- Repose Clone
- pull request
Git :
- Use of Git
- feature branch
- clone
- Add
- Commit
- Push
IDE :
- PowerBI :
- Dashboards
- Application
- DBeaver :
- Connection Process
- DB Manipulation
- Jupyter Notebook :
- Google Colaboratory
- Pycharm
Term 3
In this term, you will learn how to ace Framework and industry projects
Pyspark :
- Use of Pyspark For Data Science
- Spark Session & RDD
- Timestamp
- Schema
- Parallelize
- Broadcast Variable
- Create DataFrame
- Transformations & actions
- Empty DataFrame
- Structure type and structure field
- Select
- Collect
- WithColumn
- Where & Filter
- Drop & Drop Duplicate
- orderby and sortby
- Groupby
- Joins
- union and union all
- union byname
- map , flatmap
- Sample by vs Sample
- Pivote
- maptype
- Aggregate Functions
- Windows Function
- Read and Write in CSV
- When
- Split
- collect
- Row number
- dense rank
1) E-Commerce
2) Banking Domain
The Career Opportunities After Completing a Data Science Course in Solapur
Data Engineer: Building and maintaining data pipelines, and working with data storage solutions and tools such as AWS Glue, Redshift, and MySQL.
Quantitative Analyst: Applying quantitative techniques to financial and business data, leveraging Python for data manipulation and analysis.
Data Analyst: Performing data analysis and visualization using tools like Pandas, Matplotlib, and PowerBI, and creating actionable insights from data.
Cloud Data Engineer: Working with cloud services and data pipelines, including AWS services like S3, EMR, and Athena.
DevOps Engineer: Implementing CI/CD pipelines and managing version control with Git, focusing on automation and integration in data engineering projects.
Tools You’ll Master
Data Science Roadmap
Mentors
Python Trainer
4+ Years ITExperience
4+ Years Teaching Experience
Project Trainer
5+ Years IT Experience
5+Teaching Experience
Digital Marketing
3+ Years IT Experience
2+Teaching Experience
Manual Trainer
5+ Years IT Experience
5+Teaching Experience
Our Learners Work At
Enroll Now and get 5% Off On Course Fees
Bug Spotter Reviews
Introduction to Data Science
Data science helps us analyze and interpret large amounts of data to solve real-world problems. It’s like a detective using data to uncover patterns and insights, helping businesses make better decisions. As technology grows, data science has become essential for understanding complex information.
Why Data Science Matters
Data science is important because we’re surrounded by data daily—whether through online shopping, social media, or smartphone usage. This data helps businesses understand customer behavior, improve products, and predict trends. Data science transforms raw data into valuable insights that influence decisions across industries.
Key Components of Data Science
Data science includes several key steps:
- Data Collection: Gathering data from various sources like surveys, social media, or sensors.
- Data Cleaning: Removing errors or irrelevant information to ensure accurate analysis.
- Data Analysis: Identifying patterns and extracting meaningful insights from cleaned data.
The Process of Data Science
Data science follows a structured process: collecting, cleaning, analyzing, modeling, and interpreting data. Each step builds on the previous one to uncover insights that guide business decisions.
The Role of a Data Scientist
Data scientists analyze data to solve problems. They use skills from statistics, computer science, and business to make data-driven decisions. Their work often includes gathering data, building models, and presenting insights to business leaders.
Applications of Data Science
Data science is applied in many industries:
- Healthcare: Predicting disease outbreaks, improving patient care, and creating personalized treatments.
- E-commerce: Recommending products, forecasting trends, and enhancing customer experiences.
- Finance: Detecting fraud, managing risks, and making investment decisions.
How to Get Started in Data Science
To start learning data science:
- Beginner Courses: Look for online courses on platforms like Coursera or Udemy to learn the basics.
- Practical Projects: Work on real-world projects to gain hands-on experience and apply your skills.
Eligibility
for Data Science Course​
Any Graduate Background
You can come from any field—whether you’re an engineer, from commerce, humanities, or any other discipline. The only basic requirement is having a degree.
Interest in Coding
While prior coding experience isn’t required, having a basic interest in coding will be helpful. During the course, you’ll learn programming languages like Python, R, and SQL. Don’t worry if you’re new to coding—these skills can be learned as you progress!
Time Commitment
You’ll need to dedicate 3-4 hours per day to complete the course, including time for lectures, assignments, and hands-on exercises. Consistency and commitment are essential to your success.
No Prior Experience Required
No previous data science experience is necessary. The course is designed to start with beginner-level concepts and progress to more advanced topics, so anyone can begin from scratch and still succeed.
Curiosity & Problem-Solving Mindset
If you have a curious mindset and enjoy problem-solving, you’re well on your way. Data science involves exploring data and finding solutions to real-world problems, and this mindset will help you thrive in the field.
FAQs
Frequently asked questions
1.What is a Data Science course, and why should I consider it?
A Data Science course teaches you how to analyze large datasets to extract valuable insights. It’s in high demand as data-driven decision-making becomes essential in industries like healthcare, finance, and e-commerce.
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2.Who is eligible for a Data Science course?
Anyone with a background in math, computer science, or related fields can take a Data Science course. Many courses also welcome beginners who want to learn data analysis and programming.
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3.What skills will I learn in a Data Science course?
You’ll learn programming (Python, R, SQL), data cleaning, data visualization, statistical analysis, and machine learning. Some courses also include data engineering and big data.
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4.How long does it take to complete a Data Science course?
The duration depends on the course. Short courses take a few weeks, while more comprehensive programs, like diplomas or certifications, may take several months.
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5.Is a Data Science course difficult for beginners?
While it covers technical concepts, many courses start with the basics and progress gradually, so beginners can succeed if they practice regularly.
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6.What are the career opportunities after completing a Data Science course?
You can pursue roles like Data Analyst, Data Scientist, Machine Learning Engineer, and Business Intelligence Analyst—positions in high demand across industries.
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7.Are there any prerequisites for joining a Data Science course?
Some courses require basic knowledge of math and programming, while others are designed for beginners. Always check the course requirements before enrolling.
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8.Will I learn machine learning in a Data Science course?
Yes, machine learning is a core part of most Data Science courses, helping you build predictive models and analyze data trends.
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9.How is a Data Science course different from a Data Analytics course?
Data Science covers a broader range of topics, including machine learning and data modeling, while Data Analytics focuses more on analyzing existing data.
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10.Can I take a Data Science course online?
Yes, many top institutions offer online Data Science courses, allowing you to study at your own pace and gain flexibility.
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11.What is the Data Science Course fees in Solapur?
Data Engineer
Roles and Responsibility for Data Engineer
Roles and Responsibility for Data Engineer
The key responsibilities of a data engineer typically include:
1.Designing and building reliable data pipelines to collect, transform, and load data from multiple sources efficiently and consistently.
2.Implementing scalable data storage solutions, such as data warehouses, data lakes, or NoSQL databases, to support business intelligence, analytics, and reporting needs.
3.Creating data transformation logic to clean, enrich, and standardize data, ensuring it meets high-quality standards.
4.Collaborating with data analysts and business teams to understand their data needs and create tailored data models that align with business goals.
5.Maintaining data quality, security, and compliance by setting up monitoring, alerts, and continuous improvement practices to ensure data integrity.
6.Deploying and maintaining data infrastructure including pipelines, data storage, and related systems to ensure smooth, uninterrupted operations.
7.Sharing best practices and lessons learned with the wider data engineering community to foster collaboration and improve workflows across teams.
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Course Duration
Data Science in 3 Months?​
Can I Learn Data Science in 3 Months?
Yes, it’s absolutely possible to learn Data Science in 3 months, but it requires strong commitment and focus. To achieve this, you’ll need to dedicate 3-4 hours a day for studying, practicing, and working on hands-on exercises. It’s important not just to watch lectures, but to actively apply the concepts to real-world problems, especially in areas like Machine Learning, Data Analysis, Statistics, and Data Visualization.
Working on projects is also crucial. These projects help solidify your understanding and give you practical experience, which will make your job applications stand out. A solid portfolio of completed projects can boost your confidence and showcase your skills.
In the end, learning Data Science in 3 months is about being consistent and focused. Stick to your routine, practice regularly, and by the end of the 3 months, you’ll not only grasp key concepts but also have a portfolio that helps you secure a job in the field. With the right effort and approach, it’s definitely achievable!