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Master Data Science Course with Placement at Data Science Institute in Mumbai: Learn from basics to advanced techniques with expert guidance.

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Course Duration

100 Hrs.

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5 Project

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  • A data science course in Mumbai provides in-depth training in essential areas like data analysis, machine learning, and data visualization. This course is tailored for individuals aiming to build a career as a data scientist.
  • Joining a renowned data science institute in Mumbai ensures access to expert instructors and advanced learning resources. The curriculum covers key topics such as statistics, programming, and big data technologies, offering a comprehensive education.
  • The data scientist course in Mumbai emphasizes practical experience through hands-on projects and real-world applications. This approach helps students apply theoretical knowledge in practical scenarios, making them industry-ready.
  • Opting for data science training in Mumbai offers additional advantages, including exposure to the city's vibrant tech community and numerous networking opportunities. Mumbai's dynamic and innovative environment makes it an ideal place for pursuing a career in data science.
  • In summary, a data science course in Mumbai equips students with the necessary skills and knowledge to excel in various industries, including IT, finance, healthcare, and more

Data Science Course in Mumbai

About-Us-Course

  • The objectives of data science training in Mumbai include:
    • Core Concept Mastery: Teach essential concepts in statistics, machine learning, and data analysis.

      Hands-on Experience: Provide practical projects and real-world application opportunities.

      Tool Proficiency: Develop skills in Python, R, SQL, and Tableau.

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

      Data Visualization: Train students to effectively visualize and communicate data insights.

      Industry Readiness: Equip students with the skills needed to meet industry demands.

      Career Advancement: Offer globally recognized certification and prepare students for roles like Data Scientist and Data Analyst.

  • After completing a data science course in Mumbai with placement, salary expectations can vary based on experience, the specific employer, and the industry. Here are general salary ranges:
    • 1.Entry-Level Positions: Fresh graduates can expect to earn between 4 to 7 lakhs per annum.

      2.Mid-Level Positions: Professionals with 2-5 years of experience can expect salaries ranging from 7 to 12 lakhs per annum.

      3.Senior-Level Positions: Experienced data scientists with over 5 years in the field can earn between 12 to 20 lakhs per annum.

      4.Specialized Roles: Experts in niche areas like AI or industry-specific applications can earn over 20 lakhs per annum.

  • Completing a Data Science Online Course with placement assistance can significantly boost earning potential and career opportunities.

  • Enrolling with the best data science institute in Mumbai can significantly boost your career growth. Here are some key benefits:
    • Gain expertise in machine learning, data analysis, and visualization.

      Engage in hands-on projects and real-world applications.

      Earn a certification from a top institute for better employability.

      Connect with industry professionals, alumni, and peers.

      Secure higher-paying roles with advanced skills and certification.

      Access opportunities in IT, finance, healthcare, and e-commerce.

      Advance quickly to roles like Data Scientist, Data Analyst, and Machine Learning Engineer.

  • A data science course in Mumbai is popular for several reasons:
    • Tech Hub: Mumbai is a major tech and business hub with a high demand for data scientists.

      Quality Institutes: The city boasts reputable institutes offering comprehensive and up-to-date courses.

      Career Opportunities: Numerous companies in finance, IT, healthcare, and more seek skilled data scientists.

      Networking: Mumbai hosts various tech events, meetups, and conferences, providing excellent networking opportunities.

      Practical Experience: Courses focus on hands-on projects and real-world applications.

      Higher Salaries: The competitive job market in Mumbai often leads to attractive salary packages for data science professionals.

      Industry Connections: Strong ties between educational institutes and industry leaders enhance job placement prospects.

  • These factors contribute to the popularity of data science courses in Mumbai.

  • After completing data science training in Mumbai, you can expect to take on various roles and responsibilities, including:
    • Data Analysis: Analyse large data sets to extract meaningful insights and trends.

      Data Cleaning: Prepare and clean data to ensure accuracy and quality.

      Model Development: Create, test, and deploy machine learning models to solve business problems.

      Data Visualization: Develop visualizations and dashboards to present data findings effectively.

      Statistical Analysis: Apply statistical methods to interpret data and draw conclusions.

      Collaboration: Work with cross-functional teams, including IT, marketing, and management, to integrate data-driven solutions.

      Reporting: Prepare detailed reports and presentations to communicate insights and recommendations to stakeholders.

      Tool Proficiency: Use data science tools and programming languages such as Python, R, SQL, and Tableau.

      Problem-Solving: Identify and address business challenges using data-driven approaches.

      Continuous Learning: Stay updated with the latest trends and advancements in data science and machine learning.

  • These responsibilities will prepare you to excel in various data science roles across different industries.

  • Mumbai, being a major commercial hub, offers numerous opportunities for data scientists across various industries. Here are the top hiring industries for data scientists in Mumbai:
    • Finance and Banking: Risk management, fraud detection, investment analysis.

      Information Technology (IT): Developing solutions, improving software products.

      Healthcare: Patient care improvement, medical records management.

      E-commerce: Supply chain optimization, customer experience enhancement.

      Media and Entertainment: Viewer preference analysis, content optimization.

      Telecommunications: Network optimization, customer analytics.

      Retail: Inventory management, sales forecasting.

      Logistics and Supply Chain: Route optimization, demand forecasting.

      Real Estate: Market trend analysis, property valuation.

      Manufacturing: Predictive maintenance, quality control.

  • These sectors offer diverse and lucrative opportunities for data scientists in Mumbai.

  • After finishing a Data Science Certification Course, you will get a certificate that is valid worldwide. This certificate shows that you have the skills and knowledge needed in data science. It will help you find job opportunities in many different industries, such as IT, finance, healthcare, and retail, both in India and around the world. This certification makes you a strong candidate for data science jobs and opens up many career possibilities.

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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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FAQ's

You will learn data analysis, machine learning, data visualization, and statistical techniques.

The best course offers comprehensive content, hands-on projects, and experienced instructors.

Benefits include quality education, practical experience, networking opportunities, and industry-recognized certification.

Career options include roles such as Data Scientist, Data Analyst, Machine Learning Engineer, and Business Analyst.

While not guaranteed, these courses typically offer strong placement assistance to help secure job opportunities.

Choosing a course in Mumbai offers access to top institutes, industry connections, and a vibrant job market.

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