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- Data Science training in Indore is designed to equip students and professionals with the skills and knowledge necessary to excel in the field of data science. This comprehensive training program covers a wide range of topics, including machine learning, statistical analysis, data visualization, and programming in Python and R.
- Data Science course in Indore focuses on practical, hands-on learning, ensuring that participants can apply theoretical concepts to real-world scenarios. The curriculum is structured to provide a deep understanding of data science principles, preparing students for various roles in the industry, such as data analysts, data engineers, and data scientists.
- With a leading Data Science institute in Indore like Croma Campus, students have access to experienced instructors, state-of-the-art facilities, and the latest industry tools and techniques. These institutes emphasize a blend of classroom teaching and project-based learning, enabling students to gain practical experience and build a robust portfolio of work.
- Pursuing Data Science in Indore offers numerous advantages, including exposure to a growing tech ecosystem and opportunities to network with industry professionals. The citys vibrant job market and increasing demand for data science professionals make it an ideal location for anyone looking to advance their career in this field.
- Overall, enrolling in a Data Science course in Indore provides a comprehensive education, practical experience, and valuable industry connections, setting the stage for a successful career in data science.
Data Science Course in Indore
- Data Science training in Indore is structured to provide participants with a comprehensive understanding of data science and its applications. The primary objectives include:
- By focusing on these objectives, Data Science training in Indore ensures participants are well-prepared to excel in the fast-paced field of data science, meeting the needs of various industries.
Foundational Knowledge: Build a strong base in key data science concepts such as machine learning, data analysis, and statistical modelling.
Programming Skills: Develop expertise in programming languages commonly used in data science, including Python and R, to handle data manipulation and analysis.
Practical Application: Facilitate hands-on experience through practical projects and real-world case studies, enabling learners to apply theoretical knowledge effectively.
Data Interpretation: Enhance the ability to interpret and visualize data, transforming complex data sets into actionable insights.
Industry Relevance: Align the curriculum with current industry trends and requirements, ensuring participants are equipped with the latest tools and methodologies.
Analytical Thinking: Foster strong analytical and critical thinking skills to solve complex data problems and make data-driven decisions.
Certification Preparation: Prepare learners for professional data science certification exams, boosting their credentials and career prospects.
Career Development: Improve job readiness by training participants for high-demand roles such as data scientist, data analyst, and data engineer.
- Upon completing Data Science training in Indore, individuals can expect competitive salary packages in various roles. Here are typical salary ranges for common positions:
- These salary figures can vary based on factors such as the individual's prior experience, the specific employer, the industry, and the complexity of the role. Overall, completing Data Science training in Indore significantly enhances one's earning potential and career growth opportunities in the data science field.
Data Analyst: Entry-level data analysts can expect salaries ranging from INR 3 to 5 lakhs per annum. With a few years of experience, this can increase to INR 6 to 8 lakhs per annum.
Data Scientist: For entry-level data scientists, the average salary ranges from INR 6 to 8 lakhs per annum. With experience, this can rise to INR 10 to 15 lakhs per annum or higher.
Data Engineer: Entry-level data engineers typically earn between INR 4 to 6 lakhs per annum. Experienced data engineers can expect salaries between INR 8 to 12 lakhs per annum.
Machine Learning Engineer: Starting salaries for machine learning engineers range from INR 5 to 7 lakhs per annum. With experience, professionals can earn between INR 10 to 18 lakhs per annum.
Business Intelligence Analyst: Entry-level business intelligence analysts can expect salaries of INR 3 to 5 lakhs per annum, with potential increases to INR 6 to 10 lakhs per annum as they gain experience.
- Completing Data Science coaching in Indore enhances your career by providing valuable skills and opening up many job opportunities:
Qualify for roles like data analyst, data scientist, and machine learning engineer.
Earn competitive salaries, whether you are just starting out or have experience.
Develop key skills in data analysis, machine learning, and data visualization.
Access job opportunities in various industries, including IT, finance, healthcare, and retail.
Prepare for advanced data science certifications.
Progress to higher positions such as data science manager or chief data officer.
- A Data Science Online Course is popular because the Country is rapidly emerging as a tech hub, creating a high demand for skilled data professionals. Indore boasts several excellent training institutes that offer comprehensive, industry-aligned curricula, equipping students with essential skills in data analysis, machine learning, and data visualization.
- Additionally, the city's expanding job market provides promising career opportunities across various industries such as IT, finance, healthcare, and e-commerce. The attractive salary packages for data science roles further enhance the appeal of pursuing a data science course in Indore.
- Upon completing a Data Science course in Indore, you can expect to take on various roles and responsibilities, including:
Gather and clean data
Analyse data for trends and patterns
Build predictive models and machine learning algorithms
Create visual reports and dashboards
Work with teams to understand their data needs
Improve and update models regularly
Keep detailed records of methods and findings
Learn and apply new data science techniques
Protect data privacy and follow regulations
Help train and guide junior data scientists
- Pursuing a Data Science course in Indore at a prestigious Data Science institute in Indore can enhance your professional opportunities. Here are the primary industries employing data scientists in Indore:
Information Technology (IT) - Increasing reliance on data analytics and technology solutions.
E-commerce - Streamlining logistics and tailoring customer interactions.
Healthcare - Improving medical diagnoses and optimizing patient care.
Finance and Banking - Fraud detection and financial forecasting models.
Education - Customizing educational content and enhancing learning outcomes.
Telecommunications - Customer data analysis and network optimization.
Manufacturing - Predictive maintenance and enhancing production efficiency.
Retail - Analysing consumer behavior and inventory management.
Travel and Tourism - Enhancing travel experiences and dynamic pricing strategies.
Real Estate - Market analysis and property valuation predictions.
- Upon completion of the Data Science Certification Course, you will receive a training certificate. This comprehensive program covers all essential aspects of Data Science, including:
- The certificate validates your proficiency in these areas, demonstrating your ability to analyse, interpret, and leverage data for strategic decision-making. It is recognized worldwide, serving as proof of your skills and knowledge in Data Science. Additionally, this certificate makes you eligible to apply for related Data Science certification exams, further enhancing your professional credentials and career opportunities.
Machine Learning
Statistical Analysis
Data Visualization
Programming in Python/R
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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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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
Typically, 3 to 6 months depending on the program.
Machine learning, statistical analysis, data visualization, and programming in Python/R.
Consider faculty expertise, course content, and alumni reviews.
High demand in IT, e-commerce, healthcare, finance, and other industries.
Basic knowledge of programming and statistics is helpful but not mandatory.
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