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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 includes math and statistics, specialized programming, advanced analytics, artificial intelligence (AI), and machine learning to uncover actionable insights hidden in an organization’s data. This practice guides a business in decision-making and strategic planning. This Data Science Online Training in Kuwait makes you proficient in tools and systems used by Data Science Professionals. This course makes you skilled and efficient in Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow, and Tableau. The Data Science Online Course in Kuwait is a very good combination of theoretical and practical training. Enrolling in it makes you efficient in completing projects and POCs and helps you in finding a high-paying job in the market. Moving further, let's have a look at the course objectives of this course.
Data Science Online Training in Kuwait
- This Data Science Online Course in Kuwait helps you handle and analyze extremely large datasets using cutting-edge open-source tools and sophisticated data analysis algorithms. It prepares you for the growing demand for Big Data skills and technology and empowers professionals with data management technologies. Furthermore, this Data Science Online course in Kuwait helps you see your dreams come true. It ensures that you gain data science expertise, skills, and technology that is necessary to start a career in this domain. This Data Science Online Training in Kuwait is a ticket to getting hired by the Top Fortune Companies. It helps you get expertise and skill to come on your CV, which is the ticket to these Top Fortune Companies. Given below are some of the concepts you will learn by enrolling in this Data Science Online Course in Kuwait.
You will learn the entire Data Science process thoroughly.
This course will teach you cloud concepts & applications in Data Science.
You will learn Database concepts along with statistics fundamentals as needed in Data Science.
This course will make you aware of Visualizations for data mining and presentation.
It will provide you with an overview of Statistical Learning.
You will have an idea of the essentials of Machine Learning.
You will be learning advanced Python to apply to Data Science.
- The average fresher salary of a Data Scientist in India is around 4.0 Lakhs per annum or 33.3k per month. Furthermore, if you have more than 1 year of experience as a Data Scientist you can earn an average of Rs. 5,71,493 annually. Data Scientists having 1 to 4 years of experience earn an average of Rs. 8,00,750 per annum. Moreover, skilled and experienced professionals can earn high as 25.0 Lakhs per year. However, these are just average figures and your salary depends on your skillsets and how well you perform in your interview. Given below are some of the average salaries of professionals with data science skills in India.
Data Scientists earn an average of 10.0 Lakhs INR.
Data architects earn an average of 23.5 Lakhs INR.
Machine Learning engineers earn an average of 7.0 Lakhs INR.
Business Intelligence Developers earn an average of 6.0 Lakhs INR.
Database Engineers earn an average of 6.0 Lakhs INR.
Data Analysts earn an average of 4.3 Lakhs INR.
Data engineers earn an average of 8.0 Lakhs INR.
- Data Science is one of the most demanding skills in the market in the IT domain. Data Science Online Course in Kuwait offers you rewarding and lucrative job opportunities. It is because of its highly in-demand job role in the market due to the increased use of data in every sector. It is an excellent career with tremendous opportunities for advancement in the future. It is already in high demand and it offers competitive salaries with numerous perks. Businesses depend on data and therefore, they require skilled professionals with data science skills that are capable of studying data. Many institutes provide Data Science Online Training in Kuwait and one can enroll in them to start a career in this domain. Given below are some of the job opportunities you can explore after learning Data Science.
Data Analyst
Data Engineers
Database Administrator
Machine Learning Engineer
Data Scientist
Data Architect
Statistician
Business Analyst
Data and Analytics Manager
- Data Scientists are basically analytical data experts who have the technical skills to solve complex problems. They are responsible for extracting data from multiple sources. Moreover, these professionals use machine learning tools to organize, process, clean, validate, and analyze the data for information and patterns. Data Scientists need to develop prediction systems, present the data in a clear manner, and propose solutions and strategies. Many institutes provide Data Science Online Training in Kuwait and one can enroll in them to start a career as a Data Scientist.
Identifying valuable data sources and automating collection processes.
The need to undertake to preprocess of structured and unstructured data.
Responsible for analyzing large amounts of information to discover trends and patterns.
Building predictive models and machine-learning algorithms.
Need to combine models through ensemble modeling.
Presenting information using data visualization techniques.
Need to propose solutions and strategies to business challenges.
Responsible for collaborating with engineering and product development teams.
- There are many benefits of learning Data Science. It is one of the most trending educational topics of this era and learning it makes you rich. Data Science Online Training in Kuwait ensures that you get plenty of career opportunities from all over the world. Furthermore, this course brings great value to the table and is a highly sought-after professional in the IT field. Professionals in Data Science are the core decision-making professionals in the team in terms of data. Therefore, companies willingly pay a handsome salary to these professionals in exchange for their skills. Enrolling in the Data Science Online Training in Kuwait offers you remarkable emoluments along with an attractive job profile. Moreover, it opens numerous promising career opportunities for you and makes you more employable for these careers. Below are some of the reasons why you should learn Data Science.
Offers greater job opportunities.
Gives You Decision-Making Power.
There is Less Competition in the Field.
You will learn diverse skills and techniques.
Offers quick growth and placement.
Adds weight to your resume.
- Data Science helps a business in efficient decision-making and empowers leaders during complex business scenarios. Furthermore, this technology helps in identifying business opportunities and makes an organization capable of forecasting future market conditions. Using this technology helps employees perform better and results in increasing automation and innovation in various business processes. Moreover, it reduces business risks and threats by allowing a company to make data-driven business decisions. Due to these and various other reasons, many leading companies use it and look towards hiring professionals with Data Science Online Certification in Kuwait. Here are some of the leading companies that hire professionals with Data Science Online Certification in Kuwait.
Deloitte
PwC
Numerator
Amazon and AWS
Splunk
EY
JPMorgan Chase & Co.
Microsoft
Walmart
Databricks
- After completing this course, you will be given Data Science Online Certification in Kuwait. This certification is highly beneficial for you as it helps you build a promising and growing career. Candidates having Data Science Online Certification in Kuwait are always preferred and prioritized by employers. This certification acts as proof of your skills and expertise and helps in impressing employers. Data Science Online Certification in Kuwait shows them that you have all the expertise required to manage and assess their data for business growth and prosperity. Above all, it helps you stand out in the applicant pool when applying for a job.
- Other Related Online Training Courses in Kuwait:
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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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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...
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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...
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Data Science
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FAQ's
Data Scientists use Python programming language to clean and transform huge data sets in a form that they can work with.
Yes, Data Science is one of the fastest-growing careers and it is certainly not going to slow down. It allows you to program a machine based on multiple parameters to find the best possible business solution to a problem.
Unlike traditional application programming, Data Science takes a fundamentally different approach to building systems that provide value.
That depends on many factors, but there are plenty of certifications out there you could select that require anything from a couple of days to a couple of months of your time.
Yes, it is really worth getting a data science certification if you want to work as a Data Scientist. This certification is the best option on the table for impressing future employers and showcasing your expertise.
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