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Learn the fundamental data science concepts. Join today to become a competent data science expert.
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145 Hrs.
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we train you to get hired.
- Data Science is a dynamic analytics field that empowers professionals to act as business protectors, providing valuable insights for companies. At Croma Campus, we understand the high demand for data scientists globally, making it a promising career choice for dedicated learners in Bangalore.
- Whether you opt for self-study or a paid program, consider our classes for Data Science in Bangalore. As the top Data Science training institute in Bangalore, we are committed to helping you achieve success as a Data Scientist, offering substantial earning potential in various industries.
Data Science Course in Bangalore
- Our Data Science courses at Croma Campus in Bangalore are designed with a comprehensive approach, covering fundamental concepts and advanced data analytics. The primary objectives include:
Data Proficiency: Mastering structured and unstructured data handling for various business contexts.
Informed Decision-Making: Using data effectively to make strategic decisions that drive organizational success.
Full Skill Set: Equipping students with comprehensive data science expertise, from tools and algorithms to complex data modeling, business needs analysis, and in-depth data analysis.
Broad Learning: Covering statistics, computer science, data analytics, data visualization, and programming languages like R and Python.
Advanced Concepts: Exploring big data technologies, machine learning, artificial intelligence, and deep learning.
- Delve into the salary expectations for fresh graduates pursuing a data science career in Bangalore:
- These figures underscore the lucrative nature of data science careers, making them an attractive option for professionals starting or transitioning into this field.
Salary Landscape: The average salary of a data science expert stands at $139,000, as reported by Indeed.
Salary Insights: Glassdoor estimates the average salary for data science experts at $113,000.
Market Trends: According to PayScale, data science professionals earn around $100,000 on average.
- Our Data Science courses at Croma Campus cater to a diverse audience in Bangalore, including:
- This diversity underscores the inclusivity of Data Science Course, making them accessible to all driven by the passion for success in this dynamic field.
Undergraduate and Postgraduate Students: Enthusiastic learners looking to kickstart a rewarding career in the field of Data Scientist Classes.
IT Professionals: Experienced IT experts looking to upgrade their skills and seize data science opportunities.
Business Professionals: Executives and decision-makers eager to enhance their data analysis capabilities.
Academics and Researchers: Scholars and researchers aiming to explore data science as an academic or research specialization.
Professionals Seeking a Career Change: Individuals from various backgrounds contemplating a transition into data science for a fresh career trajectory.
- With intensive training covering a broad spectrum of data science topics, individuals can anticipate remarkable career growth upon completing our data science classes in Bangalore. Students are empowered to:
- These outcomes emphasize the broad spectrum of career advancement possibilities offered by our data science training in Bangalore, making it an appealing field for individuals seeking growth and versatility in their professional journey.
Apply Practical Knowledge: Implement acquired concepts in real-world scenarios, effectively addressing complex challenges.
Earn Certifications: Attain certifications that enhance employability and earning potential.
Secure Job Opportunities: Unlock a multitude of job openings across diverse industries and organizations.
Contribute to Industry: Play a pivotal role in the transformation of various sectors through data-driven insights and decision-making.
Obtain Leadership Positions: Possess the competence to advance into leadership roles, such as Data Science Manager, or other technical positions like Machine Learning Engineer or Data Architect.
- The future of data science is exceedingly promising, with data professionals set to play a pivotal role in harnessing the power of data across various industries in Bangalore. Key trends shaping the future scope of data science include:
- This dynamic environment is set to create exciting opportunities for data professionals in Bangalore in the years to come, making data science a field worth investing in for a bright future.
Growing Demand: The increasing reliance on data in organizations will continue to drive demand for data professionals.
Emerging Technologies: The convergence of data science with emerging technologies like IoT, Blockchain, and AI promises diverse job roles and opportunities.
Varied Job Roles: Data science professionals will have the option to explore diverse job profiles and industries.
Evolving Data Landscape: As organizations increasingly rely on data, the field of data science will adapt to changing data landscapes.
- As data science gains prominence, numerous industries in Bangalore actively seek professionals with data science expertise. The top hiring industries in data science include:
- As a leading data science training institute in Bangalore, Croma Campus prepares students to excel in these industries, providing a competitive edge in securing sought-after positions.
IT Sector: Leading tech companies like IBM, Microsoft, Google, and Infosys consistently seek skilled data scientists.
Healthcare and Medical Sector: The healthcare industry relies on data for decision-making, creating a high demand for data professionals.
Banking & Finance: Financial institutions leverage data science for risk assessment, fraud detection, and customer insights.
Transportation: Data science is essential for optimizing routes, managing logistics, and improving transportation services.
Travel Industry: The travel sector employs data science for recommendations, personalization, and enhancing the customer experience.
eCommerce: Companies like Amazon, Flipkart, and Snapdeal employ data scientists to enhance user experiences and boost sales.
Media & Entertainment: Data analysis plays a pivotal role in content recommendation, user engagement, and personalization.
Non-Profit Industries: Non-profit organizations utilize data science to drive social impact and optimize operations.
Insurance Sector: Data science helps insurers analyze risks, set premiums, and predict claims effectively.
- Embarking on a data science career opens doors to a plethora of exciting job profiles in the data-driven industry, each offering unique challenges and opportunities. Let's explore some of the prominent job roles and their corresponding salary expectations in the world of data science in Bangalore:
Data Science Classes: Data scientists work with complex datasets, uncovering patterns and trends to derive actionable insights. Their average salary stands at $139,000, as per Indeed, making it a highly rewarding profession.
Data Engineer: Data engineers design, construct, install, and maintain systems for data generation. Their role is pivotal in ensuring data availability and quality. They typically earn competitive salaries with growth potential.
Statistician: Statisticians play a vital role in analyzing data to generate valuable statistics, providing organizations with crucial insights. Their skills are in high demand, and they receive substantial compensation.
Business Intelligence Analyst: Business intelligence analysts focus on interpreting data to support informed decision-making in a company. Their proficiency in data-driven insights contributes to their attractive salaries.
Machine Learning Engineer: Machine learning engineers develop algorithms and models that power artificial intelligence applications. Their role is crucial in the development of AI solutions, making it a high-paying profession.
Data Science Manager: Data science managers oversee data science projects, ensuring teams work efficiently and effectively.
- After finishing the Data Science Certification Course at Croma Campus, you'll receive a training certificate. To earn it, you need to complete projects and tasks, with your skills checked at various times.
- It's a wise move to have a career plan, no matter where you are in your IT career. So, consider our thorough data science training in Bangalore, and don't wait any longer.
- 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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Course Duration
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Mock Interviews
Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
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WHAT OUR ALUMNI SAYS ABOUT US
Dhruv
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...
Ananya
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...
Aadarsh
Data Science
Hello, I have joined Croma Campus for a few months in the course helped in acquiring good knowledge. Also, there are many extra classes held by the trainer. There are many mock sessions given after the post completing the course. The salary offered in the industry is high to the candidates. Read more...
Pooja Yadav
Data Science
I am Pooja Yadav from Haryana. I have joined Croma Campus for learning this course. The trainer helped me to learn advanced practical knowledge and new innovative skills to enhance my ability. Also, the faculty is available in the odd hours for helping. There are many extra reference study materials Read more...
Sanjeev Kumar
Data Science
Hi, my name is Sanjeev Kumar. I have recently completed my Django Framework Certification Training from Croma Campus which is the brand name in the domain of technical education. It provides me with well-updated study material which is concerned with the latest industry trends. After the training co Read more...
Aadi
Data Science
Croma Campus has well-experienced faculties who are working as a top professional in the industry. I have recently completed my Artificial Intelligence Certification training from this institute and it gives me remarkable experience. With world-class infrastructural facilities, it has a student-frie Read more...
Vishal Jha
Data Science
Hi, my name is Vishal Jha currently I am associated with L&T. I got this amazing opportunity with the help of our specialized trainers. Due to the continuous practice through the mock interview, I get to clear out the Interview of this MNC. Apart from the training provided the behavior of trainers a Read more...
Ankit
Hi, my name is Ankit. I currently work as a Data Science at HCL. My interview in this company was scheduled by Croma Campus as I have done 2 months of Masters in Data Science course from it. I managed to crack this interview and the whole credit of my success goes to my Data Science trainer who dedi Read more...
Abhey
I have joined Croma Campus for taking the Masters in Data Science. It is a good place to learn. Helps in gaining the skills & knowledge. Read more...
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FAQ's
Croma Campus offers comprehensive Data Scientist Course In Bangalore with expert guidance and hands-on experience, ensuring you are job-ready.
Upon course completion, you'll have proficiency in data handling, analysis, and advanced data science concepts, setting you up for a successful career.
No, there are no specific prerequisites for joining our Data Scientist Course In Bangalore. We welcome learners from various backgrounds.
Your earning potential is significant, dependent on your skill level and location.
Yes, we provide placement support to help you secure a job and kick-start your data science career.
Career Assistance
- - Build an Impressive Resume
- - Get Tips from Trainer to Clear Interviews
- - Attend Mock-Up Interviews with Experts
- - Get Interviews & Get Hired
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