Best Data Science Course In Thane 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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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 Thane
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
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 a field focused on extracting meaningful insights from large sets of data to solve real-world problems. Imagine a detective using data to find patterns, trends, and valuable information that can help businesses make informed decisions. As technology continues to evolve, data science plays an increasingly important role in how we process and understand vast amounts of data.
Why Data Science Matters
Data science is essential because data surrounds us everywhere. Whether we’re shopping online, using social media, or carrying our smartphones, data is being collected continuously. This data can help businesses understand customer behavior, improve products, and predict future trends. By analyzing this data, data scientists provide insights that guide critical decisions across various industries.
Key Components of Data Science
Data science is made up of several key processes that work together to transform raw data into actionable insights.
Data Collection and Sources
The first step is gathering data from various sources like surveys, websites, social media, or sensors. High-quality data collection is essential for ensuring accurate and useful insights.Data Cleaning
Raw data often contains errors, duplicates, or irrelevant information. Data cleaning involves fixing these issues by organizing and preparing the data for analysis. Clean data ensures the accuracy of insights.Data Analysis
Once data is clean, the next step is to analyze it. This involves using different techniques to identify patterns, relationships, and trends within the data, helping to uncover valuable insights.
The Data Science Process
The typical process in data science follows a structured workflow:
- Collect data
- Clean and organize the data
- Analyze the data
- Build models
- Interpret and present findings
Each stage builds upon the previous one to deliver insights that can influence decisions.
How Data Science Projects Are Structured
Data science projects generally begin with a specific problem, such as predicting sales or identifying customer preferences. The process involves gathering relevant data, cleaning and analyzing it, building models, and then presenting the results in a way that’s easy for stakeholders to understand and act upon.
The Role of a Data Scientist
Data scientists are professionals who use data to solve problems and make data-driven decisions. They combine expertise in statistics, computer science, and domain knowledge to analyze complex data and extract actionable insights.
Key Responsibilities of Data Scientists
A data scientist’s role includes:
- Collecting and cleaning data
- Analyzing and interpreting data
- Building predictive models
- Communicating findings to business leaders to inform decisions.
Applications of Data Science
Data science is used in a variety of fields to improve processes, forecast trends, and make better decisions:
- Healthcare: Data science helps predict disease outbreaks, personalize treatments, and improve patient care.
- E-commerce: Online retailers use data science for product recommendations, trend forecasting, and enhancing customer experiences.
- Finance: Financial institutions rely on data science to detect fraud, manage risks, and make investment decisions.
How to Get Started in Data Science
If you’re interested in pursuing data science, here are some steps to help you begin:
Courses and Resources for Beginners
Many online platforms offer beginner-friendly courses in data science. Look for reputable courses that teach foundational concepts in statistics, programming, and data analysis.Practical Experience and Projects
To gain hands-on experience, work on real-world projects. Many beginners start by analyzing publicly available datasets, such as those on Kaggle or data.gov, to practice data cleaning, analysis, and visualization.
Eligibility
for Data Science Course​
Any Graduate Background
You can come from any field of study—whether it’s engineering, commerce, humanities, or any other discipline. The only requirement is that you hold a degree.Interest in Coding
While prior coding experience isn’t a must, having a basic interest in programming will be beneficial. During the course, you’ll learn programming languages such as Python, R, and SQL. Don’t worry if you’re new to coding; these skills will develop as you go!Time Commitment
To succeed in the course, you’ll need to dedicate at least 3-4 hours per day. This includes time spent on lectures, assignments, and hands-on exercises. Consistency and commitment are essential for mastering the material.No Prior Experience Required
You don’t need any prior experience in Data Science. The course is designed to guide you from the basics to more advanced concepts, so you can start fresh and still succeed.Curiosity & Problem-Solving Mindset
If you have a curious nature and enjoy solving challenges, you’re already on the right track. Data Science is all about exploring data and finding innovative solutions to real-world problems, and this mindset will be a major asset as you progress.
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. In today’s world, where data drives decision-making, this skill is in high demand, especially in industries like healthcare, finance, and e-commerce. By learning data science, you can play a crucial role in helping businesses make informed decisions based on data.2.Who is eligible for a Data Science course?
Data Science courses are open to anyone, whether you have a background in mathematics, computer science, or a related field. Many courses also welcome beginners who are interested in learning data analysis and programming, so prior experience isn’t always necessary.3.What skills will I learn in a Data Science course?
In a Data Science course, you will learn valuable skills such as programming (with languages like Python, R, and SQL), data cleaning, data visualization, statistical analysis, and machine learning. Some advanced courses may also cover topics like data engineering, big data, and deep learning.4.How long does it take to complete a Data Science course?
The duration of a Data Science course varies depending on the program. Short, introductory courses may take just a few weeks, while more comprehensive certifications or diplomas can take several months to complete. It depends on how in-depth the course material is.5.Is a Data Science course difficult for beginners?
While Data Science involves technical concepts, many courses are designed to cater to beginners. They start with basic concepts and gradually move toward advanced topics, so if you’re committed and practice regularly, you can build your knowledge step-by-step.6.What are the career opportunities after completing a Data Science course?
Upon completing a Data Science course, you can pursue various career paths such as Data Analyst, Data Scientist, Machine Learning Engineer, Business Intelligence Analyst, or Data Engineer. These roles are in high demand across industries like technology, finance, healthcare, and marketing.7.Are there any prerequisites for joining a Data Science course?
Some Data Science courses may require basic knowledge of mathematics or programming. However, many beginner courses are designed to teach everything from scratch. Always check the course requirements before enrolling to ensure it aligns with your current skill level.8.Will I learn machine learning in a Data Science course?
Yes, most Data Science courses include machine learning as part of the curriculum. Machine learning is a critical aspect of data science that allows you to build models, make predictions, and analyze patterns within large datasets.9.How is a Data Science course different from a Data Analytics course?
While both fields deal with data, Data Science courses tend to cover a broader range of topics, including programming, machine learning, and data modeling. In contrast, Data Analytics focuses more on analyzing existing data and generating insights, without diving as deeply into programming and predictive modeling.10.Can I take a Data Science course online?
Absolutely! Many renowned universities and online platforms offer Data Science courses online, providing flexibility to study at your own pace. Online courses often offer a wide range of learning formats, such as video lectures, interactive exercises, and real-world projects, making it easier to learn from anywhere.11.What is the Data Science Course fees in Thane?
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 robust, scalable data pipelines to efficiently ingest, transform, and load data from multiple sources. This involves ensuring that data flows smoothly through various stages while maintaining high performance.
2.Implementing effective data storage solutions, such as data warehouses, data lakes, or NoSQL databases, to support data analysis, reporting, and business intelligence. These systems are designed to handle large volumes of data and enable quick access for analysis.
3.Developing data transformation logic to clean, enrich, and normalize raw data, ensuring high-quality, consistent information that can be used for reporting and analysis.
4.Collaborating with data analysts and business stakeholders to understand data requirements and design appropriate data models that align with business needs and help extract meaningful insights.
5.Ensuring data quality, security, and compliance by implementing monitoring tools, setting up alerts, and establishing continuous improvement processes to address any issues related to data integrity, security, or compliance with regulations.
6.Deploying and maintaining data engineering solutions, including data pipelines, data storage systems, and supporting infrastructure, ensuring that they are reliable, efficient, and scalable.
7.Sharing knowledge and best practices with the wider data engineering community, fostering collaboration, improving standards, and contributing to the continuous development of data engineering skills and practices.
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Course Duration
Data Science in 3 Months?​
Can I Learn Data Science in 4 Months?
Yes, it’s absolutely possible to learn Data Science in 4 months, but it requires a strong commitment and focus. To achieve this, you’ll need to dedicate at least 3-4 hours every day to studying and practicing. This time should not only be spent watching videos but also solving problems, doing hands-on exercises, and applying what you learn to real-world situations. Consistent practice is key to mastering important topics like Machine Learning, Data Analysis, Statistics, and Data Visualization.
In addition to learning the theory, you should also work on projects. These projects help solidify your knowledge and give you practical experience, which is crucial for job applications. A strong portfolio of real-world projects will make you stand out to potential employers. The more projects you complete, the more confident you’ll be in your abilities.
However, learning Data Science in 4 months isn’t just about sticking to a schedule; it’s about staying focused and avoiding distractions. If you stay consistent and follow a clear plan, you can cover the key concepts and even build a solid project portfolio. By the end of 4 months, you could have a strong grasp of Data Science and be ready to apply for jobs.
In short, with the right mindset and effort, you can definitely learn Data Science and be job-ready in just 4 months.