Best Data Science Course In Akola 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
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07 December, 2024
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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 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 Akola
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
Why Python for Data Science
Python is widely favored for data science due to its simplicity, readability, and versatility. It offers an extensive collection of powerful libraries like Pandas for data manipulation, NumPy for numerical computing, Matplotlib and Seaborn for data visualization, and Scikit-learn for machine learning. Python’s rich ecosystem of tools and frameworks makes it ideal for handling diverse data science tasks, from data cleaning and analysis to advanced machine learning and deep learning. Moreover, its strong community support and vast resources make it easier for beginners and experts alike to develop data-driven solutions efficiently.
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Bug Spotter Reviews
Introduction to Data Science
Data science is about using data to find patterns and solve problems. It’s like being a detective who looks at information to uncover insights that help businesses make smarter choices. As technology grows, data science has become essential for understanding and working with large amounts of complex data.
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Why Data Science Matters
We interact with data all the time—whether we’re shopping online, browsing social media, or using apps on our phones. Data science helps businesses understand how customers behave, improve products, and predict future trends. By turning raw data into useful information, data science drives better decisions in many industries.
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Key Components of Data Science
Data science involves several important steps:
- Data Collection: Gathering data from different sources like surveys, websites, or sensors.
- Data Cleaning: Fixing mistakes and removing unnecessary information so that the data is accurate.
- Data Analysis: Looking for patterns and meaningful insights in the clean data.
The Process of Data Science
Data science is a step-by-step process:
- Collect data from different sources.
- Clean the data to remove errors and prepare it for analysis.
- Analyze the data to find patterns.
- Model the data to make predictions or test hypotheses.
- Interpret the results to make decisions based on what the data shows.
Each step builds on the previous one, helping to uncover useful insights for decision-making.
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The Role of a Data Scientist
A data scientist uses tools from fields like statistics, computer science, and business to analyze data. They gather data, create models, and present findings that help businesses solve problems. Data scientists play a key role in turning raw data into actionable insights.
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Applications of Data Science
Data science is used in many fields:
- Healthcare: Predicting disease trends, improving patient care, and creating personalized treatments.
- E-commerce: Recommending products, forecasting trends, and enhancing the shopping experience.
- Finance: Detecting fraud, managing financial risks, and helping with investment decisions.
How to Get Started in Data Science
If you’re interested in learning data science, here’s how to begin:
- Beginner Courses: Take online courses from platforms like Coursera, Udemy, or Khan Academy to learn the basics.
- Practical Projects: Work on small projects to practice what you learn and apply your skills in real-world scenarios.
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 how to analyze large datasets to uncover valuable insights. It’s highly in demand as businesses and industries—such as healthcare, finance, and e-commerce—increasingly rely on data-driven decision-making. By learning data science, you can contribute to solving complex problems and influencing strategic decisions.
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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 are interested in learning data analysis and programming skills.
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3. What skills will I learn in a Data Science course?
In a Data Science course, you’ll learn a range of important skills, including:
- Programming (Python, R, SQL)
- Data cleaning and preparing datasets for analysis
- Data visualization (creating charts and graphs to present findings)
- Statistical analysis to interpret data
- Machine learning to build predictive models
Some advanced courses may also include data engineering and working with big data technologies.
4. How long does it take to complete a Data Science course?
The duration of a Data Science course depends on its format. Short courses may take a few weeks, while more comprehensive programs, such as diplomas or certifications, may take several months to complete.
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5. Is a Data Science course difficult for beginners?
While a Data Science course covers technical concepts, many courses start with the basics and gradually introduce more complex topics. With regular practice and a curious mindset, beginners can succeed and gain strong data science skills.
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6. What are the career opportunities after completing a Data Science course?
After completing a Data Science course, you can pursue roles such as:
- Data Analyst
- Data Scientist
- Machine Learning Engineer
- Business Intelligence Analyst These positions are in high demand across industries like technology, finance, healthcare, and e-commerce.
7. Are there any prerequisites for joining a Data Science course?
Some Data Science courses may require basic knowledge of math and programming. However, there are also many courses designed for beginners, so always check the course requirements before enrolling to see if prior experience is needed.
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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. It helps you build predictive models, analyze trends, and automate decision-making processes based on data.
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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, data modeling, and predictive analysis, whereas Data Analytics focuses more on analyzing existing data to extract insights. Data Science often involves building models and forecasting future trends, while Data Analytics typically focuses on understanding and reporting historical 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. This flexibility enables you to balance your learning with other commitments, making it easier to get started and progress in the field.
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11.What is the Data Science Course fees in Akola?
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!