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Learn the fundamental data science concepts. Join today to become a competent data science expert.

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  • Data Science is a powerful analytics platform that will make discoveries and protect companies from different fraud activities. Croma Campus is a leading institution offering Best Data Science Certification Course to students looking to gain experience and enhance their skills in this growing domain.
  • Let us have a quick look at what we include as part of Data Science Certification.
    • Get a depth idea of machine learning, AI, deep learning, Big Data Hadoop, Tableau, etc.

      Become Confident enough to manage tough business requirements.

      Understand aspects of computer science, data visualizations, data analytics, and more.

      Become an established Data scientist and claim huge salary lumps.

      Improve intellectual skills along with communication and analytical skills.

Best Data Science Certification Course

About-Us-Course

  • Our Best Data Science Certification Course will help you prepare for various cloud certifications and get hired by leading industries across the world right away.
  • Here are some of our Data Science Certification Course objectives you want to consider if you are looking to make your career in the domain:
    • Well help you deliver comprehensive Data Science Course that is suitable to learn all the concepts.

      Our Data Science Certification will prepare you to work in an environment where you can make the right decisions.

      We provide you knowledge of using the tools to drive meaningful insights from it.

  • In fact, our Best Data Science Certification Courses will help you learn different aspects of computer science, data visualizations, data analytics, etc. You will also get a depth idea of machine learning, AI, deep learning, Tableau, etc.

  • There is a deficiency of 2 lac data science experts with profound logical abilities as per the latest study by McKinsey Global Institute. And the demand for skilled experts will continue to increase in the upcoming years. So, obtaining Data Science Certification Course will be the right decision for your career.
    • As per Indeed, the average salary of a Data science expert is $139K.

      As per Glassdoor, the average salary of a Data science expert is $113K.

      As per Pay Scale, the average salary of a Data science expert is $100K.

  • We help you to maintain consistent career growth and help to make yourself eligible to earn huge salary lumps.

  • Our job-oriented Data Science Course covers all necessary data science aspects that will make you industry-ready to implement those aspects practically. Also, our data science experts will guide you to earn adequate Data Science Certification and get hired by leading industries worldwide.
    • Find yourself more eligible to apply for various jobs in the domain and get hired.

      Attain skills to apply for the certification exam and get certified.

      Get certified by leveraging cloud concepts within an organization.

  • Once you complete our Best Data Science Certification Courses, you will have strong roots in the IT industry. Also, you will become a renowned and certified professional who will be recognized worldwide.

  • Theres no arguing with the fact that almost every industry releases tons of data every day that should be managed properly to drive meaningful data from it. And thats the reason, companies look for skilled professionals who could manage and transform data efficiently. Well, our Data Science Training will help genuinely you know some of the benefits of this procedure in the reel world.
    • Data Science is the no.1 skill in the IT industry with an average salary of $106K per year.

      There are over 190K jobs predicted by the next year.

      Thousands of students have already taken our Best Data Science Courses.

      The mushrooming industry is likely to reach $16 Billion by 2025.

      Almost every IT industry is looking for skilled Data Science professionals.

  • You are eligible to work in various Data Science roles that include Business Intelligence Analyst, Data Mining Engineer, Data Architect, Data Scientist, Senior Data Scientist, and more. If you also aspire to turn into a knowledgeable Data Scientist, enrolling in Best Data Science Certification Course will help you in a huge way.
  • Let us see a few common job duties that remain the same instead of the role.
    • You must know using statistical programming languages like R or Python.

      You have to perform data analysis, data congestion, data filtration, data mapping, etc.

      You should how to use big data tools like Hadoop, Hive, Map Reduce, etc.

  • As a Data Scientist, you are also responsible to design patterns to manage, process, and access data from multiple sources. You will be accountable to manage complex data models. So, do take up the Data Science Certification to turn into a master of data handling.

  • On the successful completion of the Data Science Certification Course, you can choose to work in different industries like Insurance Sector, IT Sector, Travel Industry, Transportation, Healthcare and Medical Sector, eCommerce, Banking & Finance, Non-Profit Industries, Media & Entertainment, etc. By taking up our Best Data Science Certification Course, you will get the chance to enter into industries and various industry domains too.
    • Some of the top hiring industries in Data Science include IBM, Infosys, NETFLIX, FEDEX, Accenture, Amazon, Flipkart, Snap deal, American Express, Microsoft, Google, and more.

      Withholding Data Science Certification, we assure you that you will feel more confident at the time of the interview.

      In fact, our Best Data Science Certification Courses will get placement assistance, a resume-building guide, and important instructions to crack an interview.

  • On the successful completion of the Data Science Certification Course, you will get a training certificate with us. A legit certification in Data Science Course will hence validate your skills at different levels.
    • Required to apply for different positions.

      Lucrative salary packages.

      In-depth Knowledge of the domain/field.

      Gain enough proficiency to become at the top of the cloud world.

      Open multiple job opportunities worldwide.

Why should you learn Data Science?

Not just learning –

we train you to get hired.

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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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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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FAQ's

To join the Best Data Science Certification Course, there are no special prerequisites. You must be familiar with the elementary concepts of Mathematics and Statistics.

The actual fees may differ from one training institute to another. We, at Croma Campus, offer training at a budget-friendly price.

Our certification will make students ready to take a certification exam in data science. Our tutors help students to gain the necessary skills to get a job as data scientists.

Once all your submissions are received and evaluated well, you will receive a certificate that showcases you have the right skills and knowledge.

Some of the popular job profiles for professionals include business analyst, quantitative analyst, marketing analyst, data engineer, statistician, and more. We’ll provide you with adequate training so you can understand all the concepts.

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  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired

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