Best Data Science Course In Satara 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
0%
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 Satara
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 Engineer Salary in India (INR)
| Experience Level | Salary Range (INR) |
|---|---|
| Freshers (0-1 years) | ₹4,00,000 - ₹7,00,000 |
| 2-3 years | ₹7,00,000 - ₹12,00,000 |
| 3-6 years | ₹12,00,000 - ₹18,00,000 |
| 6-10 years | ₹18,00,000 - ₹25,00,000 |
| 10-15 years | ₹25,00,000 - ₹35,00,000 |
| 15+ years | ₹35,00,000 - ₹50,00,000+ |
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 is the practice of using data to identify patterns and solve problems. Think of it as being a detective who uses information to uncover valuable insights that help businesses make better decisions. As technology advances, data science has become crucial for handling and analyzing large amounts of complex data, enabling smarter, data-driven choices.
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Why Data Science Matters
We encounter data every day—whether through online shopping, social media, or mobile apps. Data science helps businesses understand customer behavior, improve products, and forecast future trends. By transforming raw data into actionable insights, it plays a vital role in making informed decisions across industries, driving efficiency and innovation.
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Key Components of Data Science
Data science involves several core steps:
- Data Collection: Gathering data from various sources, such as surveys, websites, or sensors.
- Data Cleaning: Correcting errors and removing irrelevant or incorrect information to ensure the data is accurate and usable.
- Data Analysis: Searching for trends, patterns, and valuable insights in the cleaned data to inform decision-making.
The Process of Data Science
The process of data science typically follows these stages:
- Collect data from multiple sources.
- Clean the data to fix errors and remove any irrelevant parts.
- Analyze the data to identify trends and gain insights.
- Model the data to test hypotheses or make predictions.
- Interpret the results to guide business decisions or solve problems.
Each step builds on the previous one, creating a cycle that leads to deeper insights and more effective decisions.
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The Role of a Data Scientist
A data scientist uses tools from statistics, computer science, and business to analyze and interpret data. They gather data, build predictive models, and communicate their findings to help businesses solve complex problems. Data scientists are key players in turning raw data into meaningful, actionable insights.
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Applications of Data Science
Data science has a wide range of applications in various fields, including:
- Healthcare: Predicting disease outbreaks, improving patient care, and developing personalized treatment plans.
- E-commerce: Recommending products, forecasting market trends, and enhancing the customer shopping experience.
- Finance: Detecting fraud, managing financial risks, and making informed investment decisions.
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How to Get Started in Data Science
If you’re eager to dive into data science, here’s how you can get started:
- Beginner Courses: Begin with online courses on platforms like Coursera, Udemy, or Khan Academy to understand the basics of data science.
- Practical Projects: Apply what you learn through hands-on projects. Working on real-world problems will help solidify your skills and make your learning more practical.
Eligibility
for Data Science Course​
1.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.
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2.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!
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3.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.
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4.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.
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5.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 to analyze large datasets to uncover insights. It’s in high demand as industries like healthcare, finance, and e-commerce rely on data-driven decision-making. By learning data science, you can solve complex problems and influence key business strategies.
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 accept beginners eager to learn data analysis and programming.
3.What skills will I learn in a Data Science course?
You’ll learn:
-Programming (Python, R, SQL)
-Data cleaning and preparation
-Data visualization
-Statistical analysis
-Machine learning
Some advanced courses may cover data engineering and big data technologies.
4.How long does it take to complete a Data Science course?
Short courses take a few weeks, while comprehensive programs (diplomas or certifications) can take several months.
5.Is a Data Science course difficult for beginners?
Most courses start with the basics and gradually increase in complexity. With consistent practice, beginners can succeed and build strong data science skills.
6.What career opportunities are there after completing a Data Science course?
You can pursue roles such as:
-Data Analyst
-Data Scientist
-Machine Learning Engineer
-Business Intelligence Analyst
These roles are in high demand across industries.
7.Are there prerequisites for joining a Data Science course?
Some courses require basic knowledge of math and programming, but many are beginner-friendly. Always check the course requirements before enrolling.
8.Will I learn machine learning in a Data Science course?
Yes, machine learning is a core component of most Data Science courses, helping you build predictive models and analyze trends.
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 predictive modeling, while Data Analytics focuses on analyzing and interpreting existing data.
10.Can I take a Data Science course online?
Yes, many institutions offer online courses that let you study at your own pace, making it easier to balance learning with other commitments.
11.What is the Data Science course fee in Satara?
The Data Science course fee at Bug Spotter Software Training Institute in Akola is 30,000 Rs.
Data Engineer
Roles and Responsibility for Data Engineer
Roles and Responsibility for Data Engineer
The key responsibilities of a data engineer typically include:
- Designing and building data pipelines to collect, transform, and load data from multiple sources efficiently.
- Implementing scalable data storage solutions like data warehouses, data lakes, or NoSQL databases to support analytics and reporting.
- Creating data transformation logic to clean, enrich, and standardize data to meet quality standards.
- Collaborating with data analysts and business teams to understand data needs and develop aligned data models.
- Ensuring data quality, security, and compliance through monitoring, alerts, and continuous improvements.
- Deploying and maintaining data infrastructure to ensure seamless operations.
- Sharing best practices with the data engineering community to improve collaboration and workflows.
Course Duration
Data Science in 3 Months?​
Can I Learn Data Science in 3 Months?
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