Best Data Science Course In Mumbai With Placement
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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
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Students Trained
100%
Placement Assistance
07 December 2024
Start Date
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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 Pune 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 Mumbai
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
Emerging Technologies and Innovations
Emerging technologies in data science are transforming industries and unlocking new opportunities. Artificial intelligence (AI) and machine learning (ML) are advancing, enabling automated decision-making and predictive analytics, while deep learning and neural networks drive breakthroughs in computer vision and natural language processing (NLP). Innovations in big data tools like Apache Spark and Hadoop facilitate the processing of massive datasets, and quantum computing promises to accelerate complex data tasks. Edge computing enhances real-time data analysis on IoT devices, while explainable AI (XAI) improves model transparency. Technologies such as blockchain, federated learning, and homomorphic encryption are enhancing data privacy and security, and automated machine learning (AutoML) is simplifying model development for non-experts. These advancements are rapidly shaping the future of data science, enabling more efficient, secure, and scalable solutions.
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
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Bug Spotter Reviews
Introduction to Data Science
Data science helps us analyze and interpret large datasets to uncover insights and solve real-world problems. It’s like a detective using data to identify patterns and trends that help businesses make better decisions. As technology advances, data science has become essential in transforming massive amounts of information into actionable knowledge.
Why Data Science Matters
We interact with data every day—whether shopping online, browsing social media, or using a smartphone. Data science helps businesses leverage this information to understand customer behavior, improve products, and predict trends. It turns raw data into valuable insights, shaping decisions across industries.
Key Components of Data Science
Data Collection: The first step is gathering data from various sources, such as surveys, social media, and sensors. Quality data is critical for accurate insights.
Data Cleaning: Raw data often contains errors or irrelevant information. Cleaning the data by removing duplicates and correcting mistakes is crucial for accuracy.
Data Analysis: After cleaning, data is analyzed to find patterns and draw meaningful conclusions. This step uncovers insights that can influence business decisions.
The Process of Data Science
Data science follows a structured process: data collection, data cleaning, data analysis, modeling, and interpretation. Each step builds on the previous one to turn raw data into useful insights.
How Data Science Projects Are Structured
Data science projects typically start with a problem, such as predicting sales or identifying customer behavior. The process involves gathering and preparing data, analyzing it, and presenting findings in a clear and actionable format for decision-making.
The Role of a Data Scientist
Data scientists analyze data to inform decisions, combining expertise in statistics, computer science, and business to solve complex problems. Their work helps businesses use data effectively to make strategic choices.
Key Responsibilities of Data Scientists
- Data Gathering & Analysis: Collecting and analyzing data to identify trends and insights.
- Modeling: Building predictive models using statistical and machine learning techniques.
- Communication: Presenting findings to business leaders in an understandable way to guide decisions.
Applications of Data Science
- Healthcare: Predict disease outbreaks, personalize treatments, and improve patient care.
- E-commerce: Recommend products, forecast trends, and enhance customer experiences.
- Finance: Detect fraud, manage risk, and make informed investment decisions.
How to Get Started in Data Science
1. Courses for Beginners: Many online platforms offer beginner-friendly courses to get you started in data science.
2. Practical Experience: Start working on real-world projects using publicly available datasets. This will help you apply theoretical knowledge and gain hands-on experience.
Eligibility
for Data Science Course​
1. Any Graduate Background
- You can come from any educational background—whether you’re an engineer, from commerce, humanities, or any other field. A degree is the only basic requirement.
2. Interest in Coding
- While prior coding experience isn’t mandatory, having a basic interest in coding will help. During the course, you’ll learn programming languages like Python, R, and SQL. Don’t worry if you don’t know coding yet—these skills can be picked up as you go!
3. Time Commitment
- A minimum of 3-4 hours per day is required to complete the course, which includes time for lectures, assignments, and practical exercises. Consistency and commitment are key to your success.
4. No Prior Experience Required
- No prior data science experience is required. The course is designed to take you from beginner-level concepts all the way to advanced topics, so you can start from scratch and still succeed.
5. Curiosity & Problem-Solving Mindset
- If you have a curious mind and enjoy solving problems, you’re already on the right path. Data Science is about exploring data and finding solutions to real-world problems, and this mindset will help you excel.
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 collect, process, analyze, and interpret large sets of data to derive actionable insights. In today’s world, data-driven decision-making is essential across industries like healthcare, finance, e-commerce, and more. Pursuing a course in data science can help you become proficient in using data to solve real-world problems and make better decisions.
2. Who is Eligible for a Data Science Course?
Data Science courses are open to anyone with a background in mathematics, computer science, or related fields. However, many beginner-friendly courses are designed to help individuals with no prior experience in programming or data analysis get started. So, whether you’re an aspiring data scientist or someone looking to transition into the field, you can benefit from such courses.
3. What Skills Will I Learn in a Data Science Course?
In a Data Science course, you’ll gain a variety of skills, including:
- Programming (using languages like Python, R, SQL)
- Data cleaning and wrangling
- Data visualization (using tools like Tableau, Matplotlib, or Seaborn)
- Statistical analysis and hypothesis testing
- Machine learning algorithms (for predictive modeling)
- Big data tools (like Hadoop or Spark)
Some courses also offer knowledge of data engineering and deep learning, giving you a well-rounded skill set.
4. How Long Does it Take to Complete a Data Science Course?
The duration varies based on the course type. Shorter online bootcamps or workshops can take anywhere from a few weeks to a couple of months. Comprehensive programs, such as certifications or diplomas, typically span several months. Self-paced online courses may take longer, depending on how much time you dedicate to them.
5. Is a Data Science Course Difficult for Beginners?
While the subject can be complex, many Data Science courses are structured to accommodate beginners. They often begin with foundational concepts such as basic statistics, programming, and data visualization before progressing to more advanced topics like machine learning and artificial intelligence. Starting with a beginner-friendly course and regularly practicing your skills will help you succeed.
6. What are the Career Opportunities After Completing a Data Science Course?
Data Science professionals are in high demand across many industries. After completing a Data Science course, you could pursue various roles, such as:
- Data Analyst
- Data Scientist
- Machine Learning Engineer
- Business Intelligence Analyst
- Data Engineer These roles typically come with excellent job prospects and competitive salaries.
7. Are There Any Prerequisites for Joining a Data Science Course?
Prerequisites vary depending on the course. Some may require basic knowledge of mathematics (statistics, linear algebra) and programming, while others cater to complete beginners. Be sure to review the course requirements before enrolling to ensure you’re prepared.
8. Will I Learn Machine Learning in a Data Science Course?
Yes, machine learning is a core component of most Data Science courses. You’ll learn to use algorithms and models to make predictions, classify data, and uncover patterns from data. Topics such as supervised and unsupervised learning, deep learning, and neural networks are typically covered in more advanced modules.
9. How is a Data Science Course Different from a Data Analytics Course?
While both fields involve working with data, Data Science is broader. It focuses on extracting knowledge and insights through programming, machine learning, and statistical methods. Data Analytics, on the other hand, is more focused on analyzing and interpreting existing data to inform business decisions. In short, Data Science goes beyond analytics by also incorporating predictive modeling and algorithm development.
10. Can I Take a Data Science Course Online?
Yes, many leading institutions and platforms offer comprehensive online Data Science courses. These courses often provide flexible learning options, allowing you to study at your own pace. Platforms like Coursera, edX, and Udemy, along with university offerings, provide a variety of programs to suit different learning preferences and schedules.
11. What is the Fee for a Data Science Course in Mumbai?
At Bug Spotter Software Training Institute, the fee for a Data Science course is ₹30,000. Fees may vary depending on the institution, course duration, and level of specialization. It’s important to research different options to find a course that fits your budget and learning goals.
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 robust, scalable and reliable data pipelines to ingest, transform and load data from various sources.
- Implementing efficient data storage solutions, such as data warehouses, data lakes or NoSQL databases, to support reporting, analytics and business intelligence needs.
- Developing data transformation logic to clean, enrich and normalize data to ensure high-quality information.
- Collaborating with data analysts and business stakeholders to understand data requirements and design appropriate data models.
- Ensuring data quality, security and compliance through monitoring, alerting and continuous improvement processes.
- Deploying and maintaining data engineering solutions, including pipelines, data stores and supporting infrastructure.
- Sharing knowledge, best practices and lessons learned with the broader data engineering community.
Course Duration
Data Science in 3 Months?​
Can I Learn Data Science in 3 Months?
Yes, absolutely! It is entirely possible to learn Data Science in 3 months, but it requires a high level of commitment and consistency. To make this happen, you’ll need to dedicate a minimum of 3-4 hours daily to studying and practicing Data Science. This time should be spent not just on watching lectures, but also on working through problems, hands-on exercises, and applying the concepts you learn to real-world scenarios. Regular practice is key to mastering important topics like Machine Learning, Data Analysis, Statistics, and Data Visualization.
Moreover, it’s important to fill any gaps in your learning by working on multiple projects. These projects help reinforce your understanding and give you practical experience, which is invaluable for job applications. Building a strong portfolio of real-world projects will make you stand out when applying for jobs. The more projects you complete, the more confident you will be in your skills.
However, learning Data Science in 3 months isn’t just about following a study schedule—it’s about consistency and focus. If you stay dedicated and stick to your routine without letting distractions get in the way, you can cover the essential topics in Data Science within this time frame. By the end of 3 months, with the right mindset and effort, you will not only have a solid understanding of the core concepts but also a portfolio of projects that can help you land a job in the field.
In short, if you’re willing to put in the work and follow a structured plan, it’s definitely possible to learn Data Science and be ready for a job in just 3 months.