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  • Data Science refers to the practice of studying the data to extract meaningful insights for business. It includes domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. This entire Data Science Online Training in Saudi Arabia will make you aware of the entire Data Science process. It will help you understand Cloud concepts & applications in Data Science. Furthermore, you will get to know about Database concepts and statistics fundamentals as needed in Data Science. This course will help you learn visualizations for data mining and presentation and will provide you with an overview of Statistical Learning. It will add weight to your resume and will teach you in-demand data science skills like Statistical analysis, and Python programming with NumPy, pandas, matplotlib, and Seaborn. Moving further, let's have a look at its course objectives.

Data Science Online Training in Saudi Arabia

About-Us-Course

  • This Data Science Online Course in Saudi Arabia aims to teach you how to extract hidden patterns from unstructured data using a variety of algorithms, tools, and machine learning principles. This training helps you in making predictions and decisions using predictive causal analytics, machine learning, and prescriptive analytics. Enrolling in this Data Science Online Course in Saudi Arabia ensures that you will be able to implement different methodologies to find new trends and patterns. Furthermore, it helps you in creating quantitative algorithms to organize a massive amount of data. Data Science Online Training in Saudi Arabia provides you with the entire toolbox you need to become a data scientist. It helps in impressing interviewers by showing an understanding of the data science field. Here are some of the things you will learn by enrolling in this Data Science Online Training in Saudi Arabia.
    • It will help you understand the mathematics behind Machine Learning.

      Useful for performing linear and logistic regressions in Python.

      Creating Machine Learning algorithms in Python, using NumPy, statsmodels, and sci-kit-learn.

      You will learn how to apply your skills to real-life business cases.

      This course will help you unfold the power of deep neural networks.

      You will learn how to start coding in Python and learn how to use it for statistical analysis.

      It will help you improve Machine Learning algorithms by studying underfitting.

  • The average starting salary for Data Scientist in India is around 4.2 Lakhs per year or 35.0k per month. Data Science professionals with 1-4 years of experience can earn an average salary of Rs. 8,00,750 per annum. Above all, highly experienced professionals with Data Science Online Certification in Saudi Arabia can earn as high as 25.3 Lakhs per year. Given below are some of the estimated salaries of professionals with Data Science skills.
    • 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 promising careers of this century. It is a highly demanded skill and various companies and businesses look towards hiring professionals in it. Nowadays, every business is on the hunt to find people who can comprehend and deconstruct data. Choosing data science as a career means respecting the various disciplines on which data science as a field has been built. Data Science Online Course in Saudi Arabia teaches you everything you need to learn to start a career in this domain. Starting a career in this domain provides you with many high-paying career opportunities and makes you a part of the future. Given below are some of the job opportunities you can explore after learning Data Science.
    • Data Scientist

      Data Analyst

      Data Engineer Data Architect

      Business Intelligence Analyst

      Statistician

      Machine Learning Engineer

  • Data Scientists are analytics professionals responsible for collecting, analyzing, and interpreting data to help drive decision-making in an organization. Their job includes technical work, including mathematicians, scientists, statisticians, and computer programmers. These professionals need to work with large amounts of data to develop and test hypotheses, make inferences and analyze things. Many institutes provide Data Science Online Training in Saudi Arabia and one can enroll in them to start a career as a data scientist. Below are some of the significant roles and responsibilities of a Data Scientist.
    • Asking the right questions to begin the discovery process.

      Acquiring data and processing and cleaning the data.

      Responsible for integrating and storing data.

      Initial data investigation and exploratory data analysis.

      Choosing one or more potential models and algorithms.

      Applying data science techniques, such as machine learning.

      Measuring and improving results.

      Present the final result to stakeholders.

      Making adjustments based on feedback.

      Repeating the process to solve a new problem.

  • Learning Data Science helps you in your career growth and makes you stand out amongst the competition. It increased your earning potential and makes you more employable. Enrolling in a Data Science Online Course in Saudi Arabia enhances your chances of getting a higher salary. This domain shows exceptional growth & demand in the market and offers you endless career opportunities. Moreover, jobs in Data Science are constantly challenging and not boring. Data Science Online Course in Saudi Arabia allows you to be a part of the industry that is changing human lives in every aspect. It makes you more prestigious and makes you part of the future. Here are some of the reasons why you should learn Data Science.
    • Flexibility, freedom, and options.

      Learn the most popular data science tools.

      Keeps you updated on the latest industry trends.

      It shows you’re dedicated and committed.

      Easily showcase your expertise.

  • Using Data Science helps a business in efficient decision-making and helps empowers leaders during complex business scenarios. It helps in identifying business opportunities and makes an organization capable of forecasting future market conditions. This software technology helps employees in performing better and results in increasing automation and innovation in various business processes. Above all, it reduces business risks and threats by allowing a company to make data-driven business decisions. Due to these reasons, many leading companies use it and look towards hiring professionals with Data Science Online Certification in Saudi Arabia. Given below are some of the leading companies that hire skilled professionals in Data Science.
    • Deloitte

      PwC

      Numerator

      Amazon and AWS

      Splunk

      EY

      JPMorgan Chase & Co.

      Microsoft

      Walmart

      Databricks

  • After the completion of this course, you will be given Data Science Online Certification in Saudi Arabia. This certification is highly beneficial for you as it helps you build a promising and growing career. Candidates with Data Science Online Certification in Saudi Arabia are always preferred and prioritized by employers. This is because this certification helps in impressing employers as it acts as proof of your skills and expertise. Data Science Online Certification in Saudi Arabia 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.

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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

Data Science is a computer science field that deals with turning data into information and extracting meaningful insights from it.

Unlike traditional application programming, Data Science takes a fundamentally different approach to building systems that provide value.

It will take from 3-4 months to understand this technology and obtain a job in it. However, mastering it may take years.

Data Scientists use Python programming language to clean and transform huge data sets in a form that they can work with.

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