Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

The duration of a Data Science course can vary depending on the intensity and curriculum. Typically, it ranges from 3 to 6 months.

Data Science courses are offered in both online and offline formats. You can choose the mode that best suits your learning style and schedule.

Upon successful completion of the course, you will receive a recognized certification in Data Science.

A Data Science course covers a wide range of topics, including statistical analysis, machine learning, data mining, data visualization, and big data technologies.

You will gain hands-on experience in data cleaning, data preprocessing, data analysis, model building, and model deployment.

Yes, the course includes practical exercises, lab sessions, and real-world projects to enhance your learning.

We maintain a low student-to-instructor ratio to ensure personalized attention and support. In Batch 15-20 Students for Data Science & Gen AI Course.

At WhiteScholars Kukatpally,Hyderabad, students typically receive multiple interview opportunities based upon completing the Data Science course and their assignments score, attendance status,real-time projects, and provides strong placement support system for the students.

Real-Time Projects

• WhiteScholars: Provides Hands-on practice with real-time projects, case studies & industry problems.
• Others: Mostly theoretical knowledge with limited practical exposure.
  Instructor-Led Mentorship
• WhiteScholars: Data Scientists as Trainers are industry professionals with real-world experience.
• Others: They often rely on academic faculty members with limited industry exposure.

Placement Support

• WhiteScholars: Offers 100% placement assistance, mock interviews, resume-building sessions, CRP Sessions & 1:1 mentorship.
• Others: Job referrals or passive support.
• Student Performance-Based Interview Opportunities
• WhiteScholars: Students get multiple interview opportunities based on their student performance & project completion.
• Others: May not track student performance or offer limited referrals.
  Beginner-Friendly Curriculum
• WhiteScholars: Tailored for non-programmers and freshers or beginners starting from basics.
• Others: Often skip basic fundamentals

Career-Focused

• Regular assignments, weekly once hackathons or workshops, and career counseling sessions
• Continuous skill assessments & feedback reviews to the students

Python Core and Advanced Module Topics in the Data Science Course

The Python module in a data science course is packed with essential topics that are crucial for both beginners and advanced learners. Here’s a detailed breakdown:

Core Concepts

• Variables and Data Types
  • Understand different data types including integers, floats, and booleans.
  • Learn how to define and manipulate variables effectively.
• Strings
  • Techniques for string manipulation and formatting.
  • Methods to handle string operations efficiently.

Data Structures

• Lists: Create and manage ordered, changeable data.
• Sets: Handle collections of unique elements.
• Tuples: Work with fixed collections.
• Dictionaries: Manage data using key-value pairs.

Control Flow

• Conditional Statements: Master if-else logic for decision making.
• Loops: Efficiently iterate over sequences.

Functions and Modularity

• Functions: Create reusable blocks of code with parameters and return values.
• Modules: Learn to import and use Python modules effectively.

File Handling

• Read from and write to files.
• Manage file operations like opening, closing, and searching content.

Object-Oriented Programming

• Classes and Objects: Model real-world entities using custom classes and OOP principles.

This structured approach ensures you grasp both foundational and intricate aspects of Python for data science, making you well-equipped to tackle real-world challenges.

Embarking on an advanced data science journey with Python is both exciting and comprehensive. Here’s a detailed look at the curriculum components you can expect to explore:

1. Python Fundamentals and Advanced Topics

The foundation begins with mastering Python’s core concepts, including:

• Variables and Data Types: Understanding how Python handles different types of data.
• Data Structures: Working with lists, sets, tuples, and dictionaries to manage data efficiently.
• Control Flow: Utilizing conditional statements, loops, and functions to create dynamic programs.
• Object-Oriented Programming: Grasping classes and objects to structure software code innovatively.

2. Data Analysis and Visualization

Data analysis skills are honed using powerful libraries:

• Numerical Computing with NumPy: Performing complex mathematical operations.
• Pandas for Data Manipulation: Streamlining the organization and transformation of data.
• Matplotlib: Creating comprehensive visual stories through graphs and charts.
• Exploratory Data Analysis (EDA): Diagnosing data sets to summarize their main characteristics.

3. Handling Unstructured Data

Advanced techniques for processing unstructured data include:

• Regular Expressions: Mastery in searching and manipulating text efficiently.
• Web Scraping Projects: Collecting and analyzing data from web pages for insights.

4. Statistics and Probability

Statistics form the bedrock for data interpretation:

• Descriptive and Inferential Statistics: Summarizing data and making predictions about populations.
• Probability Distributions: Understanding different statistical distributions relevant for data modelling.

5. SQL and Data Reporting with Tools

Database proficiency and reporting are crucial:

• SQL Basics to Advanced Concepts: Crafting queries for data extraction and manipulation.
• Power BI for Visualization: Transforming raw data into insightful dashboards.
• Data Analysis Expressions (DAX): Utilizing functions for advanced analytics.

6. Machine Learning

The heart of the curriculum delves into machine learning techniques:

• Supervised Learning: Includes methods like Linear Regression, Naive Bayes, Decision Trees, and more.
• Unsupervised Learning: Focuses on Clustering methods such as K-Means and Hierarchical Clustering.
• Model Evaluation: Techniques to assess the performance of various learning algorithms.

7. Deep Learning and Neural Networks

Explore the nuances of artificial intelligence with:

• Neural Network Foundations: Structure and training of neural networks.
• Deep Learning Frameworks: Practical applications using libraries such as Keras.
• Image Classification and Preprocessing: Techniques for computer vision via CNNs.

8. Natural Language Processing (NLP)

Harnessing the power of language data:

• Text Preprocessing: Techniques for cleaning and preparing text data.
• Vectorization and NLP Libraries: Utilizing libraries like NLTK and spaCy for text analysis.
• Advanced Projects: Implementing solutions like Text Classification and Sequence Tagging.

9. Generative AI

Innovative computing is encouraged through:

• Introduction to Generative AI: Understanding concepts like language models and transformers.
• Prompt Engineering: Crafting prompts for AI models to generate sophisticated outputs.

This curriculum provides a robust framework for anyone seeking to excel in data science, leveraging the versatility and power of Python to unlock the potential of data-driven solutions.

In today’s data-driven world, the demand for skilled data professionals is booming, and India is no exception. Businesses across various sectors are keen to harness the power of data for strategic decision-making and operational efficiency. With the right certification in data science, you can unlock a wealth of career opportunities in this dynamic field.

Thriving Roles for Data Scientists

• Data Scientist: Often the centerpiece of a data team, data scientists dive deep into vast datasets to extract meaningful insights. They design algorithms and build predictive models that can propel business strategies forward.
• Data Analyst: With a focus on interpreting data, data analysts transform numbers into narratives that inform business decisions. They are adept at identifying trends and patterns through statistical analysis.
• Research Analyst: In sectors like finance and market research, research analysts delve into data to forecast trends and evaluate strategies. Their insights drive critical business decisions.
• Big Data Specialist: As organizations handle larger volumes of data, specialists in big data architecture and technologies like Hadoop and Spark become invaluable. They ensure that data pipelines are robust and efficient.
• Business Analyst Consultant/Manager: Bridging the gap between IT and business, these professionals use data to recommend changes that enhance processes, boost efficiency, and improve profits.
• Machine Learning Engineer: With the rise of AI, machine learning engineers create algorithms that automate data analysis, helping businesses predict consumer behavior and optimize operations.

Why Choose a Career in Data Science?

• High Demand: The demand for data science professionals exceeds supply, ensuring ample job opportunities across sectors like healthcare, finance, and e-commerce.
• Competitive Salaries: Skilled data scientists are among the top earners in the tech industry, with salaries reflecting the critical nature of their work.
• Career Growth: As data technology evolves, continuous learning in this field can lead to new and exciting roles, keeping your career trajectory upward.

In conclusion, certified data scientists in India are poised to excel in a landscape rich with opportunity. With the right expertise, you can carve out a successful career path in various high-impact roles that are vital across industries.

Generative AI represents a revolutionary advancement in data science, with its capabilities transforming how we understand and utilize data. Here’s why it’s crucial to any modern data science curriculum:

1. Understanding Generative AI and Language Models

Generative AI allows us to create new content, including text, images, and even music, without direct human input. This technology relies heavily on advanced language models, which are pivotal for processing and generating human-like text. As a data scientist, grasping these concepts is essential for leveraging AI to its fullest potential.

2. Mastering Prompt Engineering

Prompt engineering is a key skill when working with large language models (LLMs). It involves crafting input prompts that guide these models to deliver the desired output. This skill empowers data scientists to interact efficiently with AI models, enhancing both productivity and creativity.

3. Diverse Applications of Popular AI Models

Familiarity with popular AI models, such as:

• OpenAI: Recognized for its state-of-the-art models like ChatGPT and DALL-E.
• Gemini: A lesser-known yet impactful tool with unique capabilities.
• LLaMA: Provides capabilities for generating and fine-tuning models on specific datasets.
• LangChain: Facilitates the integration and chaining of language model functionalities to create complex applications.

These tools form the backbone of modern AI applications, making them indispensable in data science coursework.

4. Adopting AI Tools for Real-World Success

Real-world applications of generative AI extend across industries—enhancing customer service with AI-driven chatbots, automating content creation for digital marketing, and revolutionizing personalized user experiences. Data scientists need to be proficient in using these tools to drive innovation and efficiency within their organizations.

Incorporating generative AI into data science education equips students with cutting-edge knowledge and practical skills, preparing them for the next wave of technological advancements. Through learning how to harness these powerful tools, aspiring data scientists can lead the charge in the AI-driven future.

Unraveling the Fundamentals of SQL and Its Role in Data Science
Understanding SQL: The Foundation of Data Management
Structured Query Language (SQL) stands at the heart of data management and manipulation. It is an essential tool for data scientists due to its robust capability to interact with databases. Here are the basics covered in the fundamentals of SQL:

• Data Querying and Exploration: SQL is primarily used for querying databases to extract valuable insights. Using Data Query Language (DQL) and operators, it allows data scientists to filter and sort data effectively, making complex analysis simpler.
• Basic SQL Operations: Learn how to perform fundamental operations, such as selecting, inserting, updating, and deleting records from a database. These operations are crucial for maintaining and managing data efficiently.
• Database Objects: Get acquainted with SQL database objects like tables, indexes, views, and stored procedures. These components facilitate organized data storage and quick retrieval, aiding in structured data analysis.

Diving Deeper: Advanced SQL Techniques
Advanced Concepts: As you venture further, advanced SQL topics include concepts like joins, subqueries, and aggregate functions. Mastery of these allows for sophisticated data manipulation and in-depth analysis, crucial for data-driven decision-making.

Powering Insights: From SQL to Data Science
In data science, SQL’s role extends beyond mere data retrieval. Here’s how it integrates into the field:

• Data Importation and Visualization: SQL helps in importing data which can then be visualized through tools like Power BI, enhancing the comprehensibility of data trends and patterns.
• ETL Processes: SQL plays a pivotal role in Extract, Transform, Load (ETL) processes, cleansing and preparing data for analysis.
• Analysis and Calculations: With the integration of Data Analysis Expressions (DAX), SQL extends its capability to perform complex calculations, essential for deriving actionable insights from datasets.
• Security and Sharing: Manage data access and share insights securely by implementing row-level security (RLS), ensuring data integrity and confidentiality.

By harnessing the power of SQL, data scientists can effectively manipulate vast amounts of data, paving the way for actionable insights that drive business strategies.

Python Core and Advanced Module Topics in the Data Science Course

The Python module in a data science course is packed with essential topics that are crucial for both beginners and advanced learners. Here’s a detailed breakdown:

Core Concepts

• Variables and Data Types
  • Understand different data types including integers, floats, and booleans.
  • Learn how to define and manipulate variables effectively.
• Strings
  • Techniques for string manipulation and formatting.
  • Methods to handle string operations efficiently.

Data Structures

• Lists: Create and manage ordered, changeable data.
• Sets: Handle collections of unique elements.
• Tuples: Work with fixed collections.
• Dictionaries: Manage data using key-value pairs.

Control Flow

• Conditional Statements: Master if-else logic for decision making.
• Loops: Efficiently iterate over sequences.

Functions and Modularity

• Functions: Create reusable blocks of code with parameters and return values.
• Modules: Learn to import and use Python modules effectively.

File Handling

• Read from and write to files.
• Manage file operations like opening, closing, and searching content.

Object-Oriented Programming

• Classes and Objects: Model real-world entities using custom classes and OOP principles.

This structured approach ensures you grasp both foundational and intricate aspects of Python for data science, making you well-equipped to tackle real-world challenges.

Deep Learning Concepts Covered in the Data Science Course

This course provides a comprehensive overview of essential deep learning concepts that are crucial for mastering data science. Here’s a breakdown of what you can expect to learn:

• Foundation of Deep Learning: Dive into the fundamental aspects of deep learning, including understanding how it differs from traditional machine learning techniques.
• Principal Components Analysis (PCA): Discover how PCA is utilized in deep learning to reduce the dimensionality of data, improving model performance and visualization.
• Neural Network Architecture: Gain insights into the design and structure of neural networks, learning about various components such as layers, neurons, and how they are organized to solve complex problems.
• Activation Functions: Explore different activation functions, understanding their role in introducing non-linearity into the model and aiding in the learning process.
• Forward and Backward Propagation: Delve into the mechanisms of forward and backward propagation used in training neural networks, ensuring the model adjusts weights effectively to minimize errors.
• Optimizers: Learn about various optimization algorithms that are employed to enhance model training, focusing on speed and accuracy.
• Hands-on with Keras: Get practical experience using Keras for implementing regression and classification models, equipping you with the skills to build and fine-tune neural networks.

By the end of this module, you’ll have a solid understanding of deep learning principles and be prepared to apply these techniques to real-world data problems.

Key Features of Power BI in Data Science

Power BI is a robust reporting tool that plays a vital role in data science by transforming raw data into meaningful insights. Here’s a closer look at its standout features:

• Data Integration Capabilities: Power BI makes it effortless to import data from various sources. Whether it’s databases, cloud services, or simple Excel sheets, its wide array of connectors ensures you can pull in data without barriers.
• Interactive Data Visualizations: One of Power BI’s strongest features is its ability to create interactive and visually appealing reports. Users can build dashboards with dynamic visual elements like charts, graphs, and maps, allowing for easy data exploration and storytelling.
• User-Friendly Interface: With its intuitive drag-and-drop functionality, Power BI empowers users of all skill levels to create compelling reports. This accessibility lowers the technical barrier, making it a go-to tool for both beginners and seasoned data scientists.
• Powerful Data Analysis Expressions (DAX): DAX is the heart of Power BI’s analytical prowess. It allows users to perform complex calculations and build custom formulas to gain deeper insights into large datasets, all while maintaining performance efficiency.
• Advanced Reporting Features: Power BI offers advanced features like RLS (Row-Level Security) to control access to data at a granular level, ensuring that sensitive information is only visible to authorized users.
• Efficient Data Handling with Power Query: Power Query is integrated into Power BI to handle data preparation. It allows users to clean and transform data efficiently before it is loaded into the final report, simplifying the data wrangling process.
• Real-Time Data Updates: With Power BI, reports can be set to refresh in real-time, ensuring that decision-makers always have the most current information at their fingertips. This is crucial for making timely decisions in fast-paced environments.
• Seamless Collaboration and Sharing: The tool provides options for publishing reports online or embedding them into web pages, facilitating easy collaboration. Users can share insights with team members or clients, enhancing teamwork and transparency in data-driven environments.

Power BI stands out in the data science landscape due to these key features, which not only streamline the reporting process but also enhance the overall effectiveness of data-driven decision-making.

Yes, you will have lifetime access to course materials, including recorded lectures, slides, and practice exercises.

Best Institute Data Science course at WhiteScholars, you will learn Python,Machine Learning, Deep Learning, NLP, AI, SQL, Power BI,Tableau

Yes, Whitescholars Academy has a center in Kukatpally, making it convenient for learners in the area to attend data science courses.

WhiteScholars offers 100% placement assistance after completing the Best Data Science course with Gen AI , including resume and portfilo building, Corporate Readiness Program,Personal Development programs, interview preparation support, and job referrals. WhiteScholars provide strong Placement support and a robust hiring network partners , a formal job guarantee depends on individual performance and market demand conditions.

Yes, at WhiteScholars Kukatpally, Hyderabad, you will receive assignments and test after each classroom session or completion of module to improve your learning experience.

You Can Expect:

• Topic-Wise or Module-Wise Assignments: Practice what you learned in data science class—SQL queries, Python exercises, data visualizations, etc.
• Real-Time Scenarios: Basically, assignments are based on real-time data and are challenges to build practical skills.
• Doubt Clarification Support: Data Scientists as trainers assist with any questions you have while completing the assignments.

Yes, the Best Data Science course at WhiteScholars is beginner-friendly, even if you have no earlier programming experience.

WhiteScholars Data Science Certification Course in Hyderabad is beginner-friendly.

• Starts from Scratch
• Begineer-Friendly Teaching Classes.
• Hands-on real-time projects
• Learner’s Journey
• Career Support Assistance

Data Science is used for discover the patterns, build models and make predications. The techniques used in data science are Machine Learning, Deep Learning, AI, predictive Modeling. Tools covered in the data science course are Python, Numpy, Pandas, Seaborn, R, TensorFlow, and Spark.

Data Analytics is used to analyze historical data to generate actionable insights. The techniques used in data analytics are Data Manipulation, Data visulaization, and Data Mining. Tools covered in the data analytics course are Python, Numpy, Pandas, Excel, SQL, PowerBI, and Tableau.

• Call: Reach out to this number: +91 8500 52 54 56 to book your free demo session directly.
• Visit the Website: Go to www.whitescholars.com and fill out the registration form.
• Walk-In (Offline Option): Visit the WhiteScholars Academy campus at Kukatpally, Hyderabad, for in-person registration and a data science demo class.
• Online Demo: If you’re remote, ask for a live online data science course demo class via Zoom or Google Meet.

• Data Science Course Structure Overview
• Data Scientist Trainer Introduction
• Best Data Science Course Roadmap
• Career Path Guidance in Data Science
• Understand how data science is evolving in various industries

Yes, we provide regular doubt-clearing sessions and online forums for students to interact with instructors and peers.

Yes, WhiteScholars conducts hackathons and workshops to help students to understand and tackle the real-time industry scenarios.These events improves hands-on practice and problem solving skills in Data Science Course.

Data Science professionals are in high demand across various industries. After completing a data science course, you can explore job roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and more.

Benefits

• Python For Data Science: Enhance skills like Advanced Python Programming and Control Flow, Data Structures, Variables, Data Types, OOPs, Pandas, and learn Web Scraping.
• Statistics: To become a great data scientist, you need to master the mathematics and statistics in Probability Distributions.
• MLOps: By the end of this Data Science program, you will you will have hands-on experience in machine learning.
• Generative AI: Generative AI and learn to build models that create text, images, and music and build next-generation AI applications.
• Power BI: You can Master Power BI, Microsoft’s widely used data visualization tool. Learn to create interactive dashboards and management reports.
• SQL: By the end of the course, you will learn to do querying databases and data extraction and manipulation.
• Tableau: The leading data visualization tool that creates interactive dashboards
• Industry-Aligned Curriculum: Learn what companies actually expect tools, techniques, and trends that are up-to-date.
• Job-Ready Skills: Training focuses on real-time problem-solving, data handling, dashboarding, and visualization skills.

• Real-Time Projects: Work on 7+guided projects that help you to face real-world industry challenges.
• Portfolio Building: Showcase your project work to impress recruiters.

• Data Scientists as Trainers: Learn from professionals with real-time industry exposure.
• Personal Guidance: Get one-on-one mentorship to clarify concepts and track your student’s progress.

• Advanced Excel, SQL
• Power BI, Tableau
• Python for Data Analytics
• Machine Learning
• Deep Learning
• Seaborn
• Statistics
• NLP and many more

• 100% Placement Assistance: Interview preparation, resume, and portfolio building, mock interviews, and job referrals.
• Network of Hiring Partners: Partnerships with companies hiring for Data Science and Data Analytics roles.

• Weekday / Weekend Batches: Learn at your convenience.
• Online + Offline Modes: Attend in person at Kukatpally or join virtually.

• Join a growing network of data science and data analytics learners, alumni, and working professionals.
• Networking events in Hyderabad
• Alumni success stories from WhiteScholars
• Online community groups access

• Developing personalized recommendation systems:
  
  • Understanding Netflix Recommendation Engine
  • Swiggy’s dish recommendations.
• Diagnosing diseases through MRI scan analysis of body organs.
• Creating conversational AI bots to enhance customer interactions and satisfaction.
• Predicting the likelihood of fraudulent insurance claims.
• Classifying product reviews on e-commerce platforms.
• Building AI based Smart Search applications.