Awarded by Telangana Higher Education
as Emerging EdTech of India
Data Science & Gen AI Certification Course with Placement Assistance
Job Ready Course
Job Ready Course
Data Science & Gen AI
Certification Course with Placement Assistance
Job Ready Course
Job Ready Course
The best institute to kickstart your career as a Data scientist, learn Machine Learning, Gen AI, Tableau, and SQL, and enter the world of Data-Driven Decisions.
- Mode: Offline | Online | Live Recorded Sessions
- Guaranteed virtual internships with Accenture & PWC
- Certificates from Nasscom and Microsoft
- 7+ Real Time projects
- Guaranteed Interview Opportunities**
In Collaboration With
Key-Highlights
Hybrid Mode of learning (Online, Offline and access to live recorded sessions)
Guest Lectures with Industry leaders from Microsoft & IIM Faculty
10+ Capstone Projects + 1 Individual end-to-end Project
1:1 Interactions with mentors to track progress
Exclusive batches for beginners
Exclusive Community access
Corporate Readiness Program
Profile & Resume building sessions
Accreditations and Certification
Course Overview
In this Data Science Certification course, we will provide the skills you need to enhance your career in data science and prepare to become the best in the evolving field.
So by the end of the course, you will have gained industry-relevant skills in Python, SQL, Machine learning and NLP, Generative AI, Tableau, and Power BI.
The topics covered in this Data Science program and the modules you will learn are
- Module 1 – Python Fundamentals and Oops Concept
- Module 2 – Data Handling with Pandas APIs and Web Services
- Module 3 – Complete Data Visualization and Advanced Data Analysis
- Module 4 – Advanced Data Handling with Pandas and Web Scraping
- Module 5 – Statistics with Probability Distributions and Inference
- Module 6 – Advanced Machine Learning with Supervised /Unsupervised learning
- Module 7 – Natural Language Processing (NLP) with Model Deployment and Productionization
- Module 8 – Deep Learning Foundations with Training Techniques
- Module 9 – Generative AI and Tableau
- Module 10 – Advanced SQL with Power BI
- Module 11 – Integration with Other Tools
- Module 12 – Best Practices and Case Studies
- Module 13 – Advanced Neural Network Architectures and Attention Mechanisms
- Python For Data Science: Enhance skills like Advanced Python Programming and Control Flow, Data Structures, Variables and Data Types, and OOPs and 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 with
- 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
Data scientists are in high demand because industries can’t move forward without analyzing the data and extracting its insights. Since the rise of AI and machine learning, everything has been compiled into data. So, without data scientists, industries can’t move forward and make big decisions. That’s why data scientists are important in industries. They ensure that decisions are made properly by ensuring that we get the data and analyze it.
- Data Collection & Cleaning: Gather data from various sources, do the web scraping, and ensure data quality and integrity
- Exploratory Data Analysis (EDA): Understand the data distribution with summary statistics and use visualizations to identify patterns and trends
- Testing & Validation: Split data into training and test sets and apply statistical and prevent overfitting
- Model Deployment: Train machine learning models and use APIs, cloud platforms, or local servers.
- Data Visualization & Reporting: Create dashboards with tools like Tableau, Power BI
- Code Review & Documentation: keep clean, well-commented code Document assumptions and findings.
- Experimentation: Test different models with A/B testing
Tools Covered
Know Your Mentors
Swapnil
Sr. Data Analyst
20 Years Experience
Vishnu Murthy
Sr. Data Analyst
20 Years Experience
Satya Kumar
Sr. Data Scientist
12 Years Experience
Satish
Sr. Data Scientist
5 Years Experience
Program Highlights
Industry Leading Mentors
- Step into the world of Data Science with top experts from leading Industries
- Our Sessions offer unique blend of interactive and fun sessions ensuring even the toughest data concepts to be taught in a simple and engaging way.
- Learn directly from the masters who can share their real world experience and guide you through hands on activities and group discussions
Network of 25+ hiring managers
- Gain community access to 25+ Hiring managers from top tech companies.
- Our strong industry connections open doors to various job opportunities, interviews and referrals.
- We just don't teach, we connect you with the right people to land you your dream role. With Whitescholars, you are always one step ahead of getting hired.
Industry Focussed Curriculum
- Our Data Science course curriculum is designed in collaboration with Industry experts to match the real world job requirements.
- Focussed majorly on practical approach of learning so you learn what companies are looking for.
- From Python to SQL and Machine learning and PowerBI, the course is aligned with industry needs and trends.
- You will work on live projects , case studies, industry relevant projects that prepare you for real world challenges
Projects covered in the program
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1. Developing personalized recommendation systems:
- Understanding Netflix Recommendation Engine
- Swiggy’s dish recommendations
- 2. Diagnosing diseases through MRI scan analysis of body organs.
- 3. Creating a Conversational AI bots to enhance customer interactions and satisfaction.
- 4. Predicting the likelihood of fraudulent insurance claims.
- Classifying product reviews on e-commerce platforms.
- 5. Building AI based Smart Search applications
Tailored capstone projects
1.Developing personalized recommendation systems:
- Netflix Recommendation Engine
- Swiggy’s dish recommendations,
2.Diagnosing diseases through MRI scan analysis of body organs.
3.Creating a Conversational AI bot to enhance customer interactions and satisfaction.
4.Predicting the likelihood of fraudulent insurance claims.
5.Classifying product reviews on e-commerce platforms.
6.Building AI-based Smart Search applications
Resume and Portfolio building
Resume optimization : Get resume tips and upgrade it to make it more professional and impressive so it stands out to recruiters correctly. Portfolio Building : Get guidance to build a strong portfolio that shows your skills, real-time projects, and achievements in a professional way.
360 Degree Career Assistance
Resume optimization : Get resume tips and upgrade it to make it more professional and impressive so it stands out to recruiters correctly.
Portfolio Building : Get guidance to build a strong portfolio that shows your skills, real-time projects, and achievements in a professional way.
Portfolio Building : Get guidance to build a strong portfolio that shows your skills, real-time projects, and achievements in a professional way.
Get help to optimize your LinkedIn profile and make it look more professional and impressive to grab recruiters' attention, boosting your visibility, helping more people to connect, and grow your professional network.
Our CRP modules are designed to help and guide you to improve your verbal communication skills to present your idea, confidently and professionally.
Get interview tips and get prepared with mock interview sessions that help you boost confidence. Receive personalised feedback to improve your performance.
Curriculum and modules
1. Introduction to Programming and Python
- What is programming?
- History of Python
- Setting up the development environment
- IDLE
- Jupyter Notebook
- VS Code
- Writing and running your first Python program
- Comments
- Indentation
- Printing to the console
- Numbers (integers, floats)
- Strings
- Booleans
- Numbers (integers, floats)
- Strings
- Booleans
- Conditional statements (if, elif, else)
- Loops (for loops, while loops)
- Break, continue, and pass statements.
- Defining and calling functions: Function arguments and return values
- Scope and lifetime of variables in Lambda functions
- Creating and accessing lists
- List operations (indexing, slicing, adding, removing elements)
- List methods
- append
- extend
- insert
- remove
- pop
- clear
- index
- count
- sort
- Reverse
- Creating and accessing tuples
- Tuple methods
- Creating and accessing sets
- Set operations
- union
- intersection
- Difference
- Creating, accessing, and modifying dictionaries: Dictionary methods.
- keys
- values
- items
- get
- pop
- update
- String operations
- concatenation
- slicing
- formatting
- String methods
- find
- replace
- split
- join
- lower
- upper
- Strip
- Reading from files
- Writing to files
- Working with CSV files using the csv module
- Importing modules
- Standard library overview
- math
- datetime
- random
- Installing and using third-party packages (pip)
- Understanding exceptions
- Try, except, else, finally blocks
- Raising exceptions
- Classes and objects
- Attributes and methods
- Inheritance
- Polymorphism
- List comprehensions
- Dictionary comprehensions
- Set comprehensions
- Understanding and using decorators
- Creating and using generators
- Creating and using generators
- Introduction to regular expressionss
- Using the re module for pattern matching
- Introduction to Pandas
- DataFrames and Series
- Introduction to Pandas
- Reading from and writing to different file formats
- CSV
- Excel
- JSON
- Data cleaning and manipulation
- Introduction to Matplotlib and Seaborn
- Plotting graphs and charts
- Customizing plots
- Introduction to web scraping
- Using BeautifulSoup and requests
- Handling web scraping challenges
- pagination
- dynamic content
- Understanding APIs
- Making HTTP requests using requests
- Handling web scraping challenges
- Parsing JSON data
- Introduction to SQL and databases
- Using SQLite with Python
- Performing a CRUD operation
- Understanding concurrency vs. parallelism
- Using the threading and multiprocessing modules
- Writing unit tests with unittest and pytest
- Debugging techniques and tools
- Abstract classes and interfaces
- Design patterns
- Metaclasses
- Introduction to NumPy for numerical computing
- SciPy for scientific computing
- Exploring additional libraries as per interest
- TensorFlow for machine learning…………………………
2. Data Visualization
2a. Introduction to Data Visualization OOP Concepts
- Fundamentals of Data Visualization
- Importance of data visualization
- Key principles of effective data visualization
- Overview of common visualization types
- bar charts
- line charts
- scatter plots
- Introduction to Tableau and Power BI
- Introduction to Tableau: installation, interface overview
- Introduction to Power BI: installation, interface overview
2b. Data Connection and Preparation
Tableau
- Connecting to data sources (Excel, CSV, databases)
- Data cleaning and preparation
- Understanding Tableau data types and relationships
- Connecting to data sources (Excel, CSV, database s)
- Data cleaning and preparation
- Understanding Power BI data types and relationships
2c. Basic Visualization Techniques
Tableau
- Creating basic charts (bar, line, pie)
- Customizing charts (colors, labels, tooltips)
- Using filters and sorting data
- Creating basic charts (bar, line, pie) s)
- Customizing charts (colors, labels, tooltips)
- Using filters and sorting data
2d. Advanced Visualization Techniques
Tableau
- Creating advanced charts (heat maps, tree maps, bullet charts),
- Using calculated fields
- Parameters and input controls
- Creating advanced charts (heat maps, tree maps, bullet charts), using DAX (Data Analysis Expressions) for calculations, Parameters, and input controls
2e. Dashboards and Interactive Reports
Tableau
- Designing interactive dashboards,
- Adding interactivity with actions (filters, highlights),
- Creating stories for data presentation,
- Designing interactive dashboards,
- Adding interactivity with slicers and filters,
- Creating reports for data presentation.
2f. Data Preparation and Cleaning Techniques
Tableau
- Data blending,
- Data joins and unions,
- Handling null values and outliers,
- Data transformation using Power Query,
- Data joins and merges:
- Handling null values and outliers.
2g. Advanced Data Analysis
Tableau
- Time series analysis
- Forecasting and trend analysis
- Cohort analysis,
- Time series analysis,
- Forecasting and trend analysis,
- Cohort analysis
2h. Geographic and Geospatial Analysis
Tableau
- Creating maps
- Spatial joins and distance calculations,
- Advanced map visualizations,
- Creating maps,
- Spatial joins and distance calculations,
- Advanced map visualizations.
2i. Integration with Other Tools
Tableau
- Exporting Tableau visualizations,
- Embedding Tableau in websites and applications,
- Connecting Tableau to R and Python for advanced analytics,
- Exporting Power BI visualizations,
- Embedding Power BI in websites and applications,
- Connecting Power BI to R and Python for advanced analytics
2j. Best Practices and Case Studies
- Best Practices for Effective Data Visualization
- Design principles
- Avoiding common pitfalls,
- Case Studies and Real-world Applications
- Reviewing industry-specific use cases,
- Hands-on case study analysis.
3. Statistics
3a. Introduction to Statistics
- Descriptive Statistics
- Measures of central tendency: mean, median, mode.
- Measures of dispersion: range, variance, standard deviation, interquartile range.
- Data Visualization
- Histograms, bar charts, and pie charts,
- Box plots, scatter plots,
- Probability Basics
- Probability theory and rules,
- Conditional probability and Bayes’ theorem
- Probability distributions: discrete and continuous
3b. Probability Distributions
- Discrete Distributions
- Bernoulli
- Binomial
- Poisson distributions
- Continuous Distributions
- Uniform, Normal (Gaussian)
- Exponential distributions
- Central Limit Theorem
- Multivariate Distributions
- Multinomial distribution
- Multivariate Normal distribution
3c. Statistical Inference
- Sampling and Sampling Distributions
- Point Estimation and Properties of Estimators:
- Bias
- Variance
- Mean Squared Error (MSE)
- Interval Estimation
- Confidence intervals for means and proportions
- Hypothesis Testing:
- Null and alternative hypotheses
- Type I and Type II errors
- p-values and significance levels
- t-tests
- chi-square tests
- ANOVA
3d. Bayesian Statistics
- Bayesian vs Frequentist methods
- Prior, likelihood, and posterior distributions
- Bayesian inference and applications
4. Machine Learning
4a. Introduction to Machine Learning
- Overview and history of machine learning
- Types of machine learning: supervised, unsupervised, reinforcement learning
- Key terminology
- Features
- Labels
- Training set
- Test set
- Validation set
4b. Data Preprocessing
- Data cleaning
- Handling missing values
- outliers
- Feature scaling
- normalization
- standardization
- Encoding categorical variables
- one-hot encoding
- label encoding
- Feature engineering and selection techniques
4c. Supervised Learning
- Linear Models
- Linear regression
- Logistic regression
- Decision Trees
- Construction and interpretation of decision trees
- Pruning techniques to avoid overfitting
- Ensemble Methods
- Bagging (Bootstrap Aggregating)
- Random Forests
- Boosting (AdaBoost, Gradient Boosting)
- Support Vector Machines (SVM)
- Concept of hyperplane and support vectors
- Kernel tricks: linear, polynomial, radial basis function (RBF), K-Nearest Neighbors (KNN)
- Distance metrics: Euclidean, Manhattan
- Choosing the value of K
- Gradient Boosting Machines (GBMs)
- XGBoost
- LightGBM
- CatBoost
- Advanced SVM techniques
- Advanced kernel techniques
- Support Vector Regression(SVR)
4d. Unsupervised Learning
- Clustering
- K-means clustering
- Hierarchical clustering: agglomerative and divisive
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Linear Discriminant Analysis (LDA)
- Association Rule Learning
- Apriori algorithm
4e. Model Evaluation and Validation
- Train-test split
- Cross-validation
- k-fold
- stratified k-fold
- Evaluation metrics for regression
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- Mean Absolute Error (MAE)
- R²
- Evaluation metrics for classification
- accuracy
- precision
- recall
- F1 score
- ROC-AUC
4f. Introduction to Neural Networks
- Perceptron and multi-layer perceptron
- Activation functions
- sigmoid
- tanh
- ReLU
- Backpropagation and gradient descent
4g. Introduction to Time Series Analysis
- Definition and examples of time series data
- Components of time series
- trend
- seasonality
- cyclic patterns
- irregular components
- Time series vs. cross-sectional data
4h. Descriptive Analysis
- Plotting time series data
- Moving averages and smoothing techniques
- Seasonal decomposition of time series
- STL (Seasonal and Trend decomposition using Loess)
4i. Stationarity and Differencing
- Definition of stationarity
- Augmented Dickey-Fuller (ADF) test for stationarity
- Differencing to achieve stationarity
- Seasonality adjustment
4j. Autocorrelation and Partial Autocorrelation
- Autocorrelation Function (ACF)
- Partial Autocorrelation Function (PACF)
- Identifying patterns using ACF and PACF plots
4k. Time Series Models
- Autoregressive (AR) models
- AR(p) process and Yule-Walker equations
- Moving Average (MA) models
- MA(q) process and invertibility
- Autoregressive Moving Average (ARMA) models
- ARMA(p,q) process and parameter estimation
- Autoregressive Integrated Moving Average (ARIMA) models
- ARIMA(p,d,q) process, and model selection
- Seasonal ARIMA (SARIMA) models
4l. Model Evaluation and Validation
- Splitting time series data
- Training and testing sets
- Walk-forward validation
- Forecasting accuracy metrics
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- Mean Absolute Percentage Error (MAPE)
5. Advanced Machine Learning Concepts
5a. Deep Learning
- Convolutional Neural Networks (CNNs)
- Convolution and pooling layers
- Famous architectures: LeNet, AlexNet, VGG, ResNet
- Recurrent Neural Networks (RNNs)
- Vanishing gradient problem
- Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
5b. Natural Language Processing (NLP)
- Text Preprocessing
- Tokenization
- Stemming
- Lemmatization
- Stopwords removal
- Word Embeddings
- Word2Vec
- GloVe
- Sequence Models
- RNNs
- LSTMs
- GRUs
- Transformers
- Attention mechanism
- BERT
- GPT models
5c. Model Deployment and Productionization
- Model serving and APIs
- Containerization with Docker
- Model monitoring and management
- Scaling machine learning models in production
6. Deep Learning Foundations
6a. Deep Learning Basics
- Overview of deep learning and neural networks
- Key concepts
- neurons
- layers
- activation functions
- Loss functions
- Introduction to deep learning frameworks
- TensorFlow
- PyTorch
6b. Optimization and Training Techniques
- Gradient descent and variants
- SGD
- Adam
- RMSprop
- Learning rate schedules and techniques to avoid overfitting
- dropout
- regularization
- Batch normalization and its impact on training
6c. Advanced Neural Network Architectures
- Convolutional Neural Networks (CNNs)
- Review of CNNs
- Advanced CNN architectures (ResNet, DenseNet, EfficientNet)
- Techniques to improve CNN performance
- Data augmentation
- Transfer learning
- Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
- Overview of RNNs and LSTMs
- Advanced architectures: GRU, Bidirectional RNNs, and Attention mechanisms
- Applications of RNNs in time series and sequence modeling
6d. Transformers and Attention Mechanisms
- Introduction to Attention Mechanisms
- The need for attention in deep learning
- Types of attention mechanisms
- self-attention
- cross-attention
- Applications of attention in various domains
- Transformers: Theory and Architecture
- The transformer architecture
- encoder
- decoder
- multi-head attention
- Positional encoding and its importance
- Understanding the transformer model through the “Attention is All You Need” paper
Our Success Stories
Tools You Will Learn
Python
Power BI
Tableau
Generative Ai
Machine Learning
MySQL
Statistics
Large Language Model
Get Certified
Get certification after completion
of your course
Get certification after completion of your course
Get certification after completion
of your internship with us
Our Data Science Projects
Get Hands-On
Swift Testing Through Machine Learning
Mercedes-Benz leverages machine learning (ML) to enhance and accelerate the testing process of its vehicles, ensuring superior quality and performance. By using advanced ML algorithms, the company can predict potential issues, optimize testing procedures, and analyze vast amounts of data from simulations and real-world driving conditions. This approach not only reduces the time required for testing but also improves the accuracy of identifying problems, enabling faster iterations and innovations in vehicle development. As a result, Mercedes-Benz can deliver safer, more efficient, and reliable cars to the market more swiftly.
Machine Learning
Malware Prediction Through Machine Learning
Predicting server malware infections using machine learning on Microsoft servers data involves analyzing vast datasets to detect patterns indicative of malicious activity. By applying machine learning algorithms, the system can identify abnormal behavior, such as unusual traffic patterns, file access, or process anomalies, which may signal potential malware infections. Microsoft’s servers generate a massive amount of data, which machine learning models can process and learn from to predict future infections before they cause damage. This predictive approach enhances security, allowing for proactive defense measures, minimizing downtime, and protecting critical infrastructure from cyber threats.
Capstone Projects
Swiggy’s Restaurant RecommendationDeveloping a restaurant recommendation system like Swiggy using data science techniques involves creating an intelligent platform that personalizes dining choices for users. This system utilizes data science models such as collaborative filtering, content-based filtering, and machine learning algorithms to analyze user preferences, browsing history, location, and restaurant attributes (cuisine, ratings, price range). The front-end presents recommendations in an intuitive interface, while the back-end processes vast amounts of user and restaurant data in real-time. By leveraging techniques like natural language processing (NLP) for reviews and sentiment analysis, the system can provide personalized, accurate restaurant suggestions, enhancing the user experience with tailored food recommendations.
Medical Imaging Project
Early Disease Detection through Advanced Imaging Techniques
Early disease detection through advanced imaging techniques harnesses cutting-edge technologies like MRI, CT scans, and AI-driven imaging to identify medical conditions in their initial stages. These techniques allow for high-resolution, detailed views of internal organs, tissues, and structures, often detecting abnormalities before symptoms appear. With the integration of machine learning and AI, imaging systems can analyze patterns and subtle changes in medical images that may indicate early signs of diseases such as cancer, cardiovascular issues, or neurological disorders. This proactive approach leads to timely interventions, improving patient outcomes and reducing long-term healthcare costs.
Expertise in Data Science
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Statistics and Probability
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Machine Learning
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Programming Languages
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Data Wrangling
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Data Visualization
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Big Data Tools
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Database Management
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Model Evaluation and Tuning
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Natural Language Processing (NLP)
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Business Intelligence
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Ethics and Bias in AI
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Communication Skills
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Data Science Tools
Our Team: Dedicated to Your Success
Dedicated trainers committed to your success, offering exceptional knowledge and personalized guidance. From mastering skills to acing interviews, they are with you every step of the way, ensuring career growth.
Srikanth Burgula
Digital Marketing
With over 13 years of experience as a seasoned Marketing Technology professional, Srikanth specializes in crafting effective go-to-market strategies across diverse industries.
Rohitha Reddy
Digital Marketing
Rohitha's 8+ years of experience in marketing technology have equipped her with exceptional skills in customer acquisition, brand development, and market strategy
Manohar M
Senior Data Scientist
With a wealth of experience in data science, Manohar M. specializes in turning complex data into actionable insights. His expertise spans machine learning, predictive analytics, and big data technologies. We are thrilled to have Manohar lead our students toward data-driven success.
Month On Month Journey
Step-by-Step Mastery: From Basics To Internship
Basics of Mathematics & Statistics
- Descriptive Statistics
- Introduction to Probability
- Inferential Integrity
- Linear Algebra
- Exploratory Data Analysis & Data Visualization
Month 1
Python Programming
- Python Language
- Intro to Python for data science team feature selection
- Inferential Integrity
- Learn about numpy, pandas, torchvision
Month 2
Basic & Advanced Ml tools
- Decision trees and random forest test
- Clustering
- Support vector machines
- Dimensionally reduction
Month 3
Building your profile
- Github profile building
- Practice via competitions like Analytics Vidhya, Kaggle, Datahack
- Discussion forum - Kaggle discussion
Month 4
Apply for job and internship
- Identify the right jobs and apply on Naukri/LinkedIn
- Analytics Vidhya | DataJobs
- Kaggle job portal
- Internshala
- Indeed.co
Final Month
We've got answers
Frequently asked questions
Quick answers to questions you may have. Make an informed decision before embarking on your learning journey.
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
Data Science is a high-demand skill with immense career opportunities. By learning Data Science, you can work in various industries like IT, finance, healthcare, and more.
Anyone with a passion for data and a basic understanding of statistics and programming can learn Data Science. It's suitable for professionals from diverse backgrounds, including IT, engineering, finance, and business.
A basic understanding of statistics, probability, and programming languages like Python or R is recommended.
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.
The fee for a Data Science course in Hyderabad can vary depending on the institute and the specific program. Please contact us for detailed pricing information.
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.
Our instructors are highly experienced Data Science professionals with extensive industry knowledge.
We maintain a low student-to-instructor ratio to ensure personalized attention and support.
Yes, we provide regular doubt-clearing sessions and online forums for students to interact with instructors and peers.
Yes, you will have lifetime access to course materials, including recorded lectures, slides, and practice exercises.
Whitescholars Academy is one of the leading institutes offering top-notch data science courses in Hyderabad.
Yes, Whitescholars Academy has a center in Kukatpally, making it convenient for learners in the area to attend data science courses.
The fee for a data science course in Kukatpally can vary depending on the specific program and duration. Please contact us for detailed pricing information.
To learn data science effectively, you need a combination of theoretical knowledge and practical experience. Consistent practice, working on real-world projects, and staying updated with the latest trends are crucial.
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.
Ready to Start Your Journey?
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