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Learn Data Science concepts from scratch to advanced level with expert data scientist at a leading Data Science Institute in Ahmedabad.

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Course Duration

80 Hrs.

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  • A Data Science course in Ahmedabad offers comprehensive training in data analysis, machine learning, and data visualization. This course is designed for aspiring data scientists looking to enhance their skills and knowledge in the field.
  • Enrolling in a reputable Data Science institute in Ahmedabad provides students with access to experienced instructors and state-of-the-art resources. The curriculum covers essential topics such as statistics, programming, and big data technologies, ensuring a well-rounded education.
  • The Data scientist course in Ahmedabad includes hands-on projects and real-world applications, allowing students to apply their learning practically. This practical approach prepares graduates for the demands of the industry and enhances their employability.
  • Studying Data Science in Ahmedabad offers additional benefits, such as exposure to a growing tech community and networking opportunities with professionals and peers. The city's dynamic environment and focus on innovation make it an ideal location for pursuing a career in data science.
  • Overall, a data science course in Ahmedabad equips students with the skills and credentials needed to succeed in various industries, including IT, finance, healthcare, and more.

Data Science Course in Ahmedabad

About-Us-Course

  • The objectives of data science training in Ahmedabad are:
    • Teach Core Concepts: Learn statistics, machine learning, and data analysis.

      Provide Practical Experience: Work on real-world projects.

      Develop Tool Skills: Use Python, R, SQL, and Tableau.

      Enhance Analytical Skills: Improve problem-solving and data interpretation.

      Train in Data Visualization: Create clear and effective data visualizations.

      Prepare for the Industry: Get ready for real job demands.

      Boost Career Prospects: Earn a certification and prepare for roles like Data Scientist and Data Analyst.

  • These objectives help students gain the skills needed to succeed in data science.

  • After completing a data science course in Ahmedabad with placement, you can expect the following salaries:
    • Entry-Level: 4 to 7 lakhs per annum.

      Mid-Level: 7 to 12 lakhs per annum.

      Senior-Level: 12 to 20 lakhs per annum.

      Specialized Roles: Over 20 lakhs per annum.

  • A top-tier course with placement assistance significantly boosts earning potential and career opportunities.

  • Enrolling with the best data science institute in Ahmedabad can significantly enhance your career growth. Heres how:
    • Master machine learning, data analysis, and visualization.

      Work on practical projects and real-world scenarios.

      Obtain a respected certification to enhance your resume.

      Network with industry experts, alumni, and fellow students.

      Achieve higher salary potential with advanced training.

      Explore job opportunities in diverse sectors like IT, finance, healthcare, and e-commerce.

      Progress to roles such as Data Scientist, Data Analyst, and Machine Learning Engineer.

  • A data science course in Ahmedabad is popular for several reasons:
    • Growing Tech Industry: Ahmedabad's expanding tech sector creates a high demand for skilled data scientists.

      Quality Education: Reputed institutes offer comprehensive courses with experienced faculty.

      Job Opportunities: Many companies in Ahmedabad are looking for data science professionals.

      Affordable Living: Compared to other metro cities, Ahmedabad offers a lower cost of living.

      Practical Training: Courses include hands-on projects and real-world applications.

      Networking: Numerous tech events and meetups provide networking opportunities.

      Career Support: Institutes often provide placement assistance and career counseling.

  • These factors make a Data Science Online Course an attractive choice for many aspiring data scientist.

  • After completing data science training in Ahmedabad, you can expect to take on various roles and responsibilities, including:
    • Data Collection: Gather data from various sources for analysis.

      Data Cleaning: Ensure data quality by cleaning and preprocessing data.

      Model Building: Develop and implement machine learning models to address business needs.

      Data Visualization: Create visual representations of data to communicate findings clearly.

      Statistical Analysis: Use statistical techniques to interpret data and generate insights.

      Team Collaboration: Work with different departments to integrate data solutions into business strategies.

      Reporting: Generate reports and presentations to share insights with stakeholders.

      Tool Utilization: Utilize tools and languages like Python, R, SQL, and Tableau for data tasks.

      Problem-Solving: Tackle business challenges with data-driven solutions.

      Continuous Improvement: Keep up-to-date with the latest data science trends and technologies.

  • Here are the top hiring industries for data scientists who has recently completed a data science course in Ahmedabad:
    • IT: Developing solutions and improving software products.

      Finance and Banking: Risk management, fraud detection, investment analysis.

      Healthcare: Enhancing patient care, managing medical records, developing treatments.

      E-commerce: Optimizing supply chains, improving customer experiences.

      Manufacturing: Predictive maintenance, quality control, production optimization.

      Retail: Inventory management, sales forecasting, analysing consumer behaviour.

      Logistics and Supply Chain: Route optimization, demand forecasting.

      Telecommunications: Network optimization, customer analytics.

      Pharmaceuticals: Analysing clinical trial data, drug development.

      Energy and Utilities: Improving energy efficiency, resource management.

  • These sectors provide diverse opportunities for data scientists in Ahmedabad.

  • As soon as you complete a data science course in Ahmedabad, you will receive a training certificate recognized globally. This certification not only validates your skills and knowledge in data science but also significantly enhances your professional credentials.
  • With Data Science Certification, you can pursue a wide range of career opportunities across various industries worldwide, including IT, finance, healthcare, retail, and more.
  • The global recognition of your training ensures that you are well-prepared to meet the demands of the international job market, making you a competitive candidate for data science roles in diverse organizational settings.

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CURRICULUM & PROJECTS

Data Science Training Program

    NA

    • 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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    NA

    • 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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    NA

    • 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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    NA

    • 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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    NA

    • 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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    NA

    • 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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    NA

    • 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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    NA

    • 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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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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FAQ's

The course covers topics like data analysis, machine learning, statistics, and data visualization.

The best institute like Croma Campus offers comprehensive courses, experienced instructors, and strong industry connections.

Graduates can pursue roles such as Data Scientist, Data Analyst, and Machine Learning Engineer.

Ahmedabad offers quality education, growing tech opportunities, and a supportive learning environment.

Yes, we at Croma Campus offer placement assistance to help students secure jobs after completing the course.

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