Best Data Science Course In Sangli 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
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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 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 Sangli
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
Technical Skills Every Data Scientist Needs
Key technical skills every data scientist needs:
1.Programming: Proficiency in Python and R for analysis and modeling.
2.Mathematics & Statistics: Knowledge of probability, linear algebra, and calculus.
3.Data Manipulation: Using Pandas and NumPy for data cleaning and transformation.
4.Data Visualization: Tools like Matplotlib, Seaborn, and Tableau.
5.Machine Learning: Familiarity with scikit-learn, TensorFlow, and Keras.
6.Big Data: Knowledge of Hadoop, Spark, and SQL.
7.Deep Learning: Understanding of neural networks and frameworks like PyTorch.
8.Cloud Computing: Experience with AWS, Google Cloud, or Azure.
9.Version Control: Familiarity with Git.
10.Data Ethics: Awareness of privacy laws and ethical data use.
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 is the process of extracting valuable insights from large datasets using techniques from statistics, mathematics, and computer science. Think of it as being a detective—discovering hidden patterns and trends to help businesses make data-driven decisions. With the ever-increasing volume of data in today’s world, data science is essential for turning raw data into actionable intelligence.Why Data Science Matters
In today’s digital age, data is everywhere—whether we’re shopping online, using social media, or interacting with apps. Data science enables businesses to understand and analyze this data to uncover insights that guide better decision-making. By studying customer behavior, predicting future trends, and optimizing operations, data science helps organizations innovate and grow.Key Components of Data Science
- Data Collection: Gathering data from sources like sensors, surveys, social media, or public databases.
- Data Cleaning: Ensuring the data is accurate by removing errors or irrelevant information.
- Data Analysis: Using statistical methods to identify trends and patterns.
- Data Modeling: Building predictive models using machine learning techniques.
- Interpretation: Communicating findings through reports or visualizations to guide decision-making.
The Data Science Process
- Problem Definition: Clearly define the business problem you are solving (e.g., predicting sales).
- Data Collection: Gather high-quality, relevant data.
- Data Cleaning & Preparation: Clean and format the data for analysis.
- Data Exploration & Analysis: Use statistical methods and machine learning to find insights.
- Modeling & Evaluation: Build and test models for predictions or classifications.
- Interpretation & Communication: Present the findings in an understandable format for stakeholders.
How Data Science Projects Are Structured
- Define the Problem: Start with a clear goal (e.g., “How can we predict customer churn?”).
- Data Collection & Preparation: Gather and clean relevant data.
- Exploratory Data Analysis (EDA): Understand the structure of the data and identify initial patterns.
- Model Building: Develop models to predict or classify the data.
- Evaluation: Test and validate models for accuracy.
- Communication: Present results using visualizations and reports to communicate insights effectively.
The Role of a Data Scientist
A data scientist combines expertise in statistics, programming, and business to analyze data and solve problems. They create predictive models and algorithms to optimize processes, forecast trends, and provide data-driven solutions.Key Responsibilities of Data Scientists
- Data Gathering & Analysis: Collect and analyze data to uncover insights.
- Modeling & Prediction: Build models to predict future outcomes or classify data.
- Communication: Translate complex findings into clear, actionable insights for stakeholders.
- Collaboration: Work closely with cross-functional teams to solve business challenges.
Applications of Data Science
- Healthcare: Predict disease outbreaks, personalize treatments, and improve patient outcomes.
- E-commerce: Recommend products, forecast trends, and enhance customer experiences.
- Finance: Detect fraud, predict market trends, and manage risks.
- Marketing & Advertising: Optimize campaigns, personalize customer experiences, and measure effectiveness.
How to Get Started in Data Science
- Beginner Courses: Platforms like Coursera, edX, and Udemy offer courses in foundational topics such as Python programming, statistics, and machine learning.
- Hands-on Practice: Work with real-world datasets on platforms like Kaggle to build skills and gain experience.
- Learn Key Tools: Focus on learning Python, data visualization tools (e.g., Tableau or Matplotlib), and machine learning techniques.
- Build a Portfolio: Showcase your work on GitHub or personal blogs to demonstrate your abilities to potential employers.
- Join Data Science Communities: Connect with other data scientists in forums like Stack Overflow, Reddit’s r/datascience, or LinkedIn groups to learn from others, share knowledge, and stay updated.
Eligibility
for Data Science Course​
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, clean, analyze, and interpret large datasets to extract meaningful insights. In today’s data-driven world, businesses rely on data to make strategic decisions in fields like healthcare, finance, e-commerce, and more. By taking a Data Science course, you’ll learn how to leverage data to solve real-world problems, optimize processes, and make data-driven decisions.
2. Who is Eligible for a Data Science Course?
Anyone can take a Data Science course! While it’s especially helpful for individuals with a background in mathematics, computer science, or related fields, many courses are beginner-friendly and don’t require prior experience in programming or data analysis. Whether you’re new to the field or looking to shift careers, Data Science courses cater to a wide range of learners.
3. What Skills Will I Learn in a Data Science Course?
In a Data Science course, you’ll acquire essential skills that are highly valuable in the job market, including:
- Programming: Learning languages like Python, R, and SQL.
- Data Cleaning: Mastering how to fix and structure messy data.
- Data Visualization: Creating insightful charts and graphs with tools like Tableau, Matplotlib, or Seaborn.
- Statistical Analysis: Understanding trends and performing tests to make data-driven decisions.
- Machine Learning: Using algorithms to predict outcomes and classify data.
- Big Data Tools: Working with technologies like Hadoop and Spark to process large datasets.
Some courses also cover Data Engineering (building data systems) and Deep Learning (advanced machine learning).
4. How Long Does It Take to Complete a Data Science Course?
The duration of a Data Science course depends on the type:
- Bootcamps and workshops: A few weeks to a couple of months.
- Comprehensive certification programs: Typically several months.
- Self-paced online courses: Varies based on how much time you can dedicate to learning.
5. Is a Data Science Course Difficult for Beginners?
While Data Science can seem complex, many courses are designed to be beginner-friendly. They start with foundational concepts like basic statistics, programming, and data visualization, and then gradually introduce more advanced topics such as machine learning. With regular practice and dedication, beginners can succeed.
6. What Are the Career Opportunities After Completing a Data Science Course?
The demand for Data Science professionals is high, and there are many career opportunities in this field, including:
- Data Analyst: Analyze data to provide actionable insights.
- Data Scientist: Build models and make data-driven predictions.
- Machine Learning Engineer: Develop systems that learn from data.
- Business Intelligence Analyst: Help businesses make data-driven decisions.
- Data Engineer: Build the infrastructure for managing and processing data.
These roles offer excellent pay and career growth potential.
7. Are There Any Prerequisites for Joining a Data Science Course?
Some courses may require a basic understanding of mathematics (like statistics or linear algebra) and programming. However, there are also beginner courses that don’t require any prior knowledge, so you can start from scratch if you’re new to the field.
8. Will I Learn Machine Learning in a Data Science Course?
Yes! Machine Learning is an integral part of most Data Science courses. You’ll learn how to build predictive models, classify data, and find patterns using algorithms. Topics like supervised learning, unsupervised learning, and deep learning are typically covered in the advanced sections of the course.
9. How is a Data Science Course Different from a Data Analytics Course?
Both Data Science and Data Analytics involve working with data, but they differ in scope:
- Data Science is broader—it includes data analysis, programming, machine learning, and predictive modeling.
- Data Analytics focuses more on analyzing existing data to make business decisions without much emphasis on building predictive models.
In short, Data Science is more about using algorithms and programming to predict future trends, while Data Analytics is about understanding current data to make decisions.
10. Can I Take a Data Science Course Online?
Yes, there are many online Data Science courses available on platforms like Coursera, edX, and Udemy, as well as offerings from universities. Online learning provides flexibility, allowing you to study at your own pace and from anywhere.
11. What is the Fee for a Data Science Course in Sangli?
The fee for a Data Science course can vary. For example, at Bug Spotter Software Training Institute in Mumbai, the fee is ₹30,000. Prices can differ depending on the institution, course duration, and specialization. It’s a good idea to compare different options to find a course that fits your budget and goals.
Data Engineer
Roles and Responsibility for Data Engineer
Roles and Responsibility for Data Engineer
A Data Engineer typically has the following key responsibilities:
Designing and Building Scalable Data Pipelines: Create robust and reliable pipelines to ingest, transform, and load data from various sources, ensuring seamless data flow.
Implementing Data Storage Solutions: Set up and manage data storage systems like data warehouses, data lakes, and NoSQL databases to support business intelligence, analytics, and reporting.
Data Transformation: Develop processes to clean, enrich, and normalize data, ensuring that it is accurate, consistent, and high-quality for analysis.
Collaboration with Analysts and Stakeholders: Work closely with data analysts and business teams to understand data requirements, and design effective data models that meet organizational needs.
Ensuring Data Quality and Security: Implement monitoring, alerting, and compliance measures to maintain data quality, security, and compliance, driving continuous improvement.
Deploying and Maintaining Data Systems: Oversee the deployment and maintenance of data infrastructure, including pipelines, data stores, and supporting systems, ensuring reliability and performance.
Knowledge Sharing: Contribute to the data engineering community by sharing best practices, solutions, and lessons learned with peers.
Course Duration
Data Science in 3 Months?​
Can I Learn Data Science in 3 Months?
Yes, learning Data Science in 3 months is definitely achievable, but it requires dedication and consistent effort. You’ll need to allocate 3-4 hours a day to focus on key concepts like Machine Learning, Data Analysis, Statistics, and Data Visualization. The key is not just watching lectures but actively engaging in hands-on practice, working through exercises, and applying what you learn to real-world projects.
Building a portfolio by completing several projects is crucial to demonstrating your skills and making you stand out to potential employers. A strong portfolio will showcase your ability to apply theoretical knowledge to practical problems, which is essential for landing a job.
Consistency, focus, and a structured study plan are vital for mastering the core skills in a short time. By the end of the 3 months, with the right effort and mindset, you’ll have a solid understanding of Data Science concepts and practical experience that positions you as job-ready.
With determination and the right approach, you can absolutely start a career in Data Science in just 3 months!