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we train you to get hired.
- Enrolling in a data science course in Hyderabad is a great way to start a career in tech. Hyderabad is a major IT hub with many opportunities. A good data science training institute in Hyderabad offers courses that cover key topics like machine learning, statistics, and data visualization.
- The data science training in Hyderabad includes hands-on practice with real data. Experienced professionals teach these courses, sharing useful insights and the latest trends. The city also has a lively tech community and many events for networking with experts and employers.
- Taking a data science course in Hyderabad gives you the skills and knowledge needed to succeed in this exciting field.
Data Science Course in Hyderabad
- The data science institute in Hyderabad offers a data scientist course in Hyderabad designed to meet several key training objectives:
Mastering Data Science Fundamentals: Students will gain a solid understanding of the core concepts of data science, including statistics, machine learning, and data analysis techniques.
Practical Experience: The course emphasizes hands-on training, allowing students to work with real-world data sets and tools used in the industry.
Technical Proficiency: Training covers essential data science tools and programming languages such as Python, R, SQL, and big data technologies to ensure students are technically adept.
Analytical Skills: The course focuses on enhancing analytical and problem-solving skills, enabling students to interpret data effectively and make data-driven decisions.
Industry-Relevant Projects: Students will engage in projects that reflect current industry practices, providing practical experience and preparing them for real-world data science roles.
Career Readiness: The institute aims to prepare students for successful careers as data scientists by providing knowledge of the latest industry trends and best practices.
- After completing the best data science course in Hyderabad, salary expectations vary:
- Completing a top-tier course in Hyderabad can significantly boost earning potential and career prospects.
Entry-Level: 5 to 8 lakhs per annum for fresh graduates.
Mid-Level: 8 to 15 lakhs per annum for 2-5 years of experience.
Senior-Level: 15 to 25 lakhs per annum for over 5 years of experience.
Specialized Roles: Over 25 lakhs per annum for expertise in niche areas like AI or industry-specific applications.
- Enrolling with the best data science institute in Hyderabad can significantly enhance your career growth. Heres how:
Skill Development: Acquire in-depth knowledge of key data science concepts, including machine learning, data analysis, and visualization.
Practical Training: Gain hands-on experience with real-world data sets and projects, making you job-ready.
Industry Certification: Earning a certification from a top institute boosts your resume and makes you more attractive to employers.
Networking: Connect with industry experts, alumni, and peers, expanding your professional network and job opportunities.
Increased Salary Potential: With advanced skills and certification, you can secure higher-paying roles in the job market.
Versatile Career Options: Open doors to various industries such as finance, healthcare, retail, and technology, where data science skills are highly sought after.
Rapid Career Advancement: Move up the career ladder quickly to roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and Data Science Manager.
- A data science course in Hyderabad is popular due to several key factors:
- These factors make data science training in Hyderabad a sought-after choice.
Tech Hub: Hyderabad is a major IT hub, creating high demand for data scientists.
Quality Education: Reputed institutes offer comprehensive programs with experienced faculty.
Job Opportunities: Numerous tech companies provide ample career prospects for data science professionals.
Networking: The city hosts tech events and meetups, offering excellent networking opportunities.
Competitive Salaries: Data science professionals can expect attractive salaries in Hyderabad.
Innovation: The city's focus on innovation and research fosters a strong learning environment.
Support: Government and industry initiatives enhance education and job readiness.
- After learning Data Science Online Course, you can expect to take on various roles and responsibilities, including:
- These roles and responsibilities enable data scientists to contribute significantly to business success by leveraging data to make informed decisions and drive growth.
Data Collection and Processing: Gathering data from various sources and preparing it for analysis.
Data Analysis: Analysing data to uncover patterns, trends, and insights that can inform business decisions.
Model Development: Creating, testing, and implementing predictive models using machine learning algorithms.
Data Visualization: Designing and producing clear, compelling visualizations to communicate data findings.
Statistical Analysis: Applying statistical methods to understand data distributions, relationships, and significances.
Tool Utilization: Using tools and programming languages such as Python, R, SQL, and Tableau to manipulate and analyse data.
Collaboration: Working with other teams, such as IT, marketing, and operations, to integrate data-driven insights into various business processes.
Reporting: Preparing reports and presentations to share findings with stakeholders, providing actionable insights.
Problem-Solving: Identifying business problems that can be solved with data science, proposing solutions, and implementing them.
Continuous Improvement: Keeping up with the latest data science trends, techniques, and technologies to continuously improve skills and methods.
- After completing data science training in Hyderabad with placement, you can find job opportunities in several top hiring industries:
- Completing data science training in Hyderabad with placement can open doors to these diverse and dynamic industries, offering ample opportunities for career growth.
Information Technology (IT): Major IT companies and tech startups frequently hire data scientists to analyse big data and improve software solutions.
Finance and Banking: Financial institutions use data science for risk management, fraud detection, and investment strategies.
Healthcare: Hospitals and healthcare companies rely on data scientists to improve patient care, develop new treatments, and manage healthcare data.
E-commerce: Online retailers use data science to enhance customer experience, optimize supply chains, and personalize marketing.
Telecommunications: Telecom companies leverage data science for network optimization, customer analytics, and improving service delivery.
Retail: Retailers use data science for inventory management, sales forecasting, and understanding consumer behaviour.
Manufacturing: Manufacturers apply data science for predictive maintenance, quality control, and optimizing production processes.
Energy: Energy companies use data science to improve efficiency, manage resources, and develop smart grids.
Logistics and Supply Chain: These industries use data science for route optimization, demand forecasting, and inventory management.
Media and Entertainment: Companies use data science to analyse viewer preferences, optimize content delivery, and enhance user engagement.
- As soon as you complete a Data Science Certification Course, you will receive a training certificate valid worldwide. This certification enhances your credentials and opens up global career opportunities across various industries.
- You May Also Read:
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CURRICULUM & PROJECTS
Data Science Training Program
- Introduction To Python
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Version Control with Git & GitHub
- Python Keyword and Identiers
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Introduction To Variables
- Variables, expression condition and function
- Global and Local Variables in Python
- Packing and Unpacking Arguments
- Type Casting in Python
- Byte objects vs. string in Python
- Variable Scope
- Python Data Type
- Declaring and using Numeric data types
- Using string data type and string operations
- Understanding Non-numeric data types
- Understanding the concept of Casting and Boolean.
- Strings
- List
- Tuples
- Dictionary
- Sets
- Control Structure & Flow
- Statements if, else, elif
- How to use nested IF and Else in Python
- Loops
- Loops and Control Statements.
- Jumping Statements Break, Continue, pass
- Looping techniques in Python
- How to use Range function in Loop
- Programs for printing Patterns in Python
- How to use if and else with Loop
- Use of Switch Function in Loop
- Elegant way of Python Iteration
- Generator in Python
- How to use nested Loop in Python
- Use If and Else in for and While Loop
- Examples of Looping with Break and Continue Statement
- How to use IN or NOT IN keyword in Python Loop.
- Python Function, Modules and Packages
- Python Syntax
- Function Call
- Return Statement
- Arguments in a function Required, Default, Positional, Variable-length
- Write an Empty Function in Python pass statement.
- Lamda/ Anonymous Function
- *args and **kwargs
- Help function in Python
- Scope and Life Time of Variable in Python Function
- Nested Loop in Python Function
- Recursive Function and Its Advantage and Disadvantage
- Organizing python codes using functions
- Organizing python projects into modules
- Importing own module as well as external modules
- Understanding Packages
- Random functions in python
- Programming using functions, modules & external packages
- Map, Filter and Reduce function with Lambda Function
- More example of Python Function
- List
- What is List.
- List Creation
- List Length
- List Append
- List Insert
- List Remove
- List Append & Extend using + and Keyword
- List Delete
- List related Keyword in Python
- List Revers
- List Sorting
- List having Multiple Reference
- String Split to create a List
- List Indexing
- List Slicing
- List count and Looping
- List Comprehension and Nested Comprehension
- Tuple
- What is Tuple
- Tuple Creation
- Accessing Elements in Tuple
- Changing a Tuple
- Tuple Deletion
- Tuple Count
- Tuple Index
- Tuple Membership
- TupleBuilt in Function (Length, Sort)
- Dictionary
- Dict Creation
- Dict Access (Accessing Dict Values)
- Dict Get Method
- Dict Add or Modify Elements
- Dict Copy
- Dict From Keys.
- Dict Items
- Dict Keys (Updating, Removing and Iterating)
- Dict Values
- Dict Comprehension
- Default Dictionaries
- Ordered Dictionaries
- Looping Dictionaries
- Dict useful methods (Pop, Pop Item, Str , Update etc.)
- Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- Strings
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Python Exception Handling
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specic Exception in Python
- Raising Exception
- Try and Finally
- Python File Handling
- Opening a File
- Python File Modes
- Closing File
- Writing to a File
- Reading from a File
- Renaming and Deleting Files in Python
- Python Directory and File Management
- List Directories and Files
- Making New Directory
- Changing Directory
- Python Database Interaction
- Basic SQL, DDL and DML commands
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- Reading an excel
- Working With Excel
- Reading an excel le using Python
- Writing to an excel sheet using Python
- Python| Reading an excel le
- Python | Writing an excel le
- Adjusting Rows and Column using Python
- ArithmeticOperation in Excel le.
- Play with Workbook, Sheets and Cells in Excel using Python
- Creating and Removing Sheets
- Formatting the Excel File Data
- More example of Python Function
- Complete Understanding of OS Module of Python
- Check Dirs. (exist or not)
- How to split path and extension
- How to get user prole detail
- Get the path of Desktop, Documents, Downloads etc.
- Handle the File System Organization using OS
- How to get any les and folders details using OS
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- Data Analysis and Visualization using Pandas.
- Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
- How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
- Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalues
- Exploring the Data Plotting, Correlations, and Histograms
- Getting statistical information about the data Analysis Concepts, Handle the None Values
- Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
- Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
- Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
- Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
- Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
- Exporting the results to Excel Joins
- Python | Pandas Data Frame Inner Join
- Under sting the properties of Data Frame Left Join (Left Outer Join)
- Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
- Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
- Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
- Pandas | How to Group Data How to apply Lambda / Function on Data Frame
- Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- Data Analysis and Visualization using NumPy
- Introduction to NumPy Numerical Python
- Importing NumPy and Its Properties
- NumPy Arrays
- Creating an Array from a CSV
- Operations an Array from a CSV
- Operations with NumPy Arrays
- Two-Dimensional Array
- Selecting Elements from 1-D Array
- Selecting Elements from 2-D Array
- Logical Operation with Arrays
- Indexing NumPy elements using conditionals
- NumPys Mean and Axis
- NumPys Mode, Median and Sum Function
- NumPys Sort Function and More
- Data Analysis and Visualization using MatPlotLib
- Bar Chart using Python MatPlotLib
- Column Chart using Python MatPlotLib
- Pie Chart using Python MatPlotLib
- Area Chart using Python MatPlotLib
- Scatter Plot Chart using Python MatPlotLib
- Play with Charts Properties Using MatPlotLib
- Export the Chart as Image
- Understanding plt. subplots () notation
- Legend Alignment of Chart using MatPlotLib
- Create Charts as Image
- Other Useful Properties of Charts.
- Complete Understanding of Histograms
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Introduction to Data Visualization with Seaborn
- Introduction to Seaborn
- Making a scatter plot with lists
- Making a count plot with a list
- Using Pandas with seaborn
- Tidy vs Untidy data
- Making a count plot with a Dataframe
- Adding a third variable with hue
- Hue and scattera plots
- Hue and count plots
- Visualizing Two Quantitative Variables
- Visualizing a Categorical and a Quantitative Variable
- Customizing Seaborn Plots
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- Foundation for AI: Learn traditional ML models, evaluation, and workflows.
- Introduction to ML, AI, and Deep Learning
- Types of ML (Supervised, Unsupervised, Reinforcement)
- ML Pipeline: Data Cleaning, Feature Engineering
- Common ML Algorithms: Linear, Logistic, DT, RF, SVM, KNN
- Model Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
- Overfitting, Underfitting, Cross-Validation
- Hands-on Project: Titanic Dataset (or similar)
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- Understand the inner workings of neural networks and train them with Keras.
- Introduction to Neural Networks & Deep Learning
- Activation Functions (ReLU, Sigmoid, Tanh)
- Feedforward Neural Network
- Backpropagation & Gradient Descent
- Learning Rate, Schedulers & Optimizers (SGD, Adam, RMSProp)
- Softmax, Cross-Entropy Loss
- Keras Basics: Sequential API & Functional API
- Fully Connected Layer Forward/Backward Pass
- Regularization Dropout, Batch Normalization
- Data Preprocessing & Data Augmentation
- Weight Initialization Strategies
- Babysitting Learning: Overfit detection, TensorBoard Monitoring
- Hands-on: MLP on MNIST / Tabular data (e.g. HR Analytics)
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- Master CNNs, object detection, segmentation, and deployment.
- Basics of Images, Image Preprocessing
- Convolution: 2D Conv, Forward & Backward
- Pooling, Padding, Stride, Transposed Conv
- CNN Architectures: LeNet, AlexNet, VGG, ResNet
- GPU vs CPU for DL
- Transfer Learning: Inception, MobileNet, fine-tuning
- Semantic Segmentation using UNet
- Object Detection YOLO, SSD, Region Proposal
- Bounding Box Regressor
- Siamese Networks for Similarity Search
- Hands-on:
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- Train text models from scratch and with BERT.
- Introduction to NLP and Use Cases
- Preprocessing: Tokenization, Lemmatization, Stopwords, Normalization
- Feature Extraction: BOW, TF-IDF, N-Grams
- Word Embeddings: Word2Vec, GloVe, Dense Vectors
- POS Tagging, Named Entity Recognition
- RNN, LSTM Forward Pass and BPTT
- Advanced LSTM Applications + Architectures
- Attention Mechanism + Encoder-Decoder
- Transformers, BERT, Hugging Face Pipelines
- NLP Evaluation Metrics: BLEU, ROUGE
- Hands-on:
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- Take models from notebooks to real-world applications.
- Saving & Loading Models (Pickle, Joblib, Keras)
- Flask vs FastAPI Serving ML models
- Streamlit/Gradio for Web Apps
- Hosting Models on Hugging Face Spaces, Streamlit Cloud
- MLflow Intro Model Tracking & Versioning
- Hands-on:
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- Build, evaluate, and deploy a mini AI project end-to-end.
- Project Selection: Tabular, CV, or NLP
- Data Collection/Exploration
- Preprocessing + Feature Engineering
- Model Training & Tuning
- Evaluation & Interpretation
- App Creation (Streamlit/Gradio)
- Deployment + Final Presentation/Submission
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1. In recent times Python learning is hugely in demand. And I was looking for an institution to get myself enrolled in this course to become a Data Scientist. They have the best trainers and an affordable fee structure. It's been months now and I am fully satisfied with the trainers, live sessions, Read more...
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Data Science
Enrolling yourself in the Deep learning certification program of Croma Campus is the best way to master all the concepts of data science and deep learning. My experience with these people has been very amazing and positive. The support/guidance I got from the trainer is highly commendable Thanks, te Read more...
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FAQ's
You will learn data analysis, machine learning, and data visualization techniques.
The most reputable institute offers comprehensive courses and experienced instructors.
The course includes training in data processing, statistical analysis, and real-world project experience.
A top institute provides quality education, hands-on practice, and industry connections.
You can pursue roles like Data Scientist, Data Analyst, and Machine Learning Engineer.
Enrolling ensures access to top-tier education, experienced faculty, and better job placement opportunities.
Croma Campus is among the best institutes for Data Science coaching in Hyderabad, offering expert-led training, real-world projects, and placement assistance. Their industry-focused curriculum ensures students gain practical skills and knowledge for a successful Data Science career.
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