- 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.
- 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.
- Other Related Online Training Courses in Saudi Arabia:
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CURRICULUM & PROJECTS
Data Science Certification Training
- Creation of Excel Sheet Data
- Range Name, Format Painter
- Conditional Formatting, Wrap Text, Merge & Centre
- Sort, Filter, Advance Filter
- Different type of Chart Creations
- Auditing, (Trace Precedents, Trace Dependents)Print Area
- Data Validations, Consolidate, Subtotal
- What if Analysis (Data Table, Goal Seek, Scenario)
- Solver, Freeze Panes
- Various Simple Functions in Excel(Sum, Average, Max, Min)
- Real Life Assignment work
- Advance Data Sorting
- Multi-level sorting
- Restoring data to original order after performing sorting
- Sort by icons
- Sort by colours
- Lookup Functions
- Lookup
- VLookup
- HLookup
- Subtotal, Multi-Level Subtotal
- Grouping Features
- Column Wise
- Row Wise
- Consolidation With Several Worksheets
- Filter
- Auto Filter
- Advance Filter
- Printing of Raw & Column Heading on Each Page
- Workbook Protection and Worksheet Protection
- Specified Range Protection in Worksheet
- Excel Data Analysis
- Goal Seek
- Scenario Manager
- Data Table
- Advance use of Data Tables in Excel
- Reporting and Information Representation
- Pivot Table
- Pivot Chat
- Slicer with Pivot Table & Chart
- Generating MIS Report In Excel
- Advance Functions of Excel
- Math & Trig Functions
- Text Functions
- Lookup & Reference Function
- Logical Functions & Date and Time Functions
- Database Functions
- Statistical Functions
- Financial Functions
- Functions for Calculation Depreciation
- SQL Server 2019 Installation
- Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
- SQL Server Features & Purpose
- Using Management Studio (SSMS)
- Conguration Tools & SQLCMD
- Conventions & Collation
- SQL Database Architecture
- Database Creation using GUI
- Database Creation using T-SQL scripts
- DB Design using Files and File Groups
- File locations and Size parameters
- Database Structure modications
- SQL Server Database Tables
- Table creation using T-SQL Scripts
- Naming Conventions for Columns
- Single Row and Multi-Row Inserts
- Table Aliases
- Column Aliases & Usage
- Table creation using Schemas
- Basic INSERT
- UPDATE
- DELETE
- SELECT queries and Schemas
- Use of WHERE, IN and BETWEEN
- Variants of SELECT statement
- ORDER BY
- GROUPING
- HAVING
- ROWCOUNT and CUBE Functions
- Table creation using Constraints
- NULL and IDENTITY properties
- UNIQUE KEY Constraint and NOT NULL
- PRIMARY KEY Constraint & Usage
- CHECK and DEFAULT Constraints
- Naming Composite Primary Keys
- Disabling Constraints & Other Options
- Benets of Views in SQL Database
- Views on Tables and Views
- SCHEMA BINDING and ENCRYPTION
- Issues with Views and ALTER TABLE
- Common System Views and Metadata
- Common Dynamic Management views
- Working with JOINS inside views
- Need for Indexes & Usage
- Indexing Table & View Columns
- Index SCAN and SEEK
- INCLUDED Indexes & Usage
- Materializing Views (storage level)
- Composite Indexed Columns & Keys
- Indexes and Table Constraints
- Primary Keys & Non-Clustered Indexes
- Why to use Stored Procedures
- Types of Stored Procedures
- Use of Variables and parameters
- SCHEMABINDING and ENCRYPTION
- INPUT and OUTPUT parameters
- System level Stored Procedures
- Dynamic SQL and parameterization
- Scalar Valued Functions
- Types of Table Valued Functions
- SCHEMABINDING and ENCRYPTION
- System Functions and usage
- Date Functions
- Time Functions
- String and Operational Functions
- ROW_COUNT
- GROUPING Functions
- Why to use Triggers
- DML Triggers and Performance impact
- INSERTED and DELETED memory tables
- Data Audit operations & Sampling
- Database Triggers and Server Triggers
- Bulk Operations with Triggers
- Cursor declaration and Life cycle
- STATIC
- DYNAMIC
- SCROLL Cursors
- FORWARD_ONLY and LOCAL Cursors
- KEYSET Cursors with Complex SPs
- ACID Properties and Scope
- EXPLICIT Transaction types
- IMPLICIT Transactions and options
- AUTOCOMMIT Transaction and usage
- Overview of BI concepts
- Why we need BI
- Introduction to SSBI
- SSBI Tools
- Why Power BI
- What is Power BI
- Building Blocks of Power BI
- Getting started with Power BI Desktop
- Get Power BI Tools
- Introduction to Tools and Terminology
- Dashboard in Minutes
- Interacting with your Dashboards
- Sharing Dashboards and Reports
- Power BI Desktop
- Extracting data from various sources
- Workspaces in Power BI
- Data Transformation
- Query Editor
- Connecting Power BI Desktop to our Data Sources
- Editing Rows
- Understanding Append Queries
- Editing Columns
- Replacing Values
- Formatting Data
- Pivoting and Unpivoting Columns
- Splitting Columns
- Creating a New Group for our Queries
- Introducing the Star Schema
- Duplicating and Referencing Queries
- Creating the Dimension Tables
- Entering Data Manually
- Merging Queries
- Finishing the Dimension Table
- Introducing the another DimensionTable
- Creating an Index Column
- Duplicating Columns and Extracting Information
- Creating Conditional Columns
- Creating the FACT Table
- Performing Basic Mathematical Operations
- Improving Performance and Loading Data into the Data Model
- Introduction to Modelling
- Modelling Data
- Manage Data Relationship
- Optimize Data Models
- Cardinality and Cross Filtering
- Default Summarization & Sort by
- Creating Calculated Columns
- Creating Measures & Quick Measures
- What is DAX
- Data Types in DAX
- Calculation Types
- Syntax, Functions, Context Options
- DAX Functions
- Date and Time
- Time Intelligence
- Information
- Logical
- Mathematical
- Statistical
- Text and Aggregate
- Measures in DAX
- Measures and Calculated Columns
- ROW Context and Filter Context in DAX
- Operators in DAX - Real-time Usage
- Quick Measures in DAX - Auto validations
- In-Memory Processing DAX Performance
- How to use Visual in Power BI
- What Are Custom Visuals
- Creating Visualisations and Colour Formatting
- Setting Sort Order
- Scatter & Bubble Charts & Play Axis
- Tooltips and Slicers, Timeline Slicers & Sync Slicers
- Cross Filtering and Highlighting
- Visual, Page and Report Level Filters
- Drill Down/Up
- Hierarchies and Reference/Constant Lines
- Tables, Matrices & Conditional Formatting
- KPI's, Cards & Gauges
- Map Visualizations
- Custom Visuals
- Managing and Arranging
- Drill through and Custom Report Themes
- Grouping and Binning and Selection Pane, Bookmarks & Buttons
- Data Binding and Power BI Report Server
- Why Dashboard and Dashboard vs Reports
- Creating Dashboards
- Conguring a Dashboard Dashboard Tiles, Pinning Tiles
- Power BI Q&A
- Quick Insights in Power BI
- Custom Data Gateways
- Exploring live connections to data with Power BI
- Connecting directly to SQL Server
- Connectivity with CSV & Text Files
- Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel
- Content packs
- Update content packs
- Introduction and Sharing Options Overview
- Publish from Power BI Desktop and Publish to Web
- Share Dashboard with Power BI Service
- Workspaces (Power BI Pro) and Content Packs (Power BI Pro)
- Print or Save as PDF and Row Level Security (Power BI Pro)
- Export Data from a Visualization
- Export to PowerPoint and Sharing Options Summary
- Understanding Data Refresh
- Personal Gateway (Power BI Pro and 64-bit Windows)
- Replacing a Dataset and Troubleshooting Refreshing
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
- 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
- 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
- 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.
- 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 Reverse
- 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
- 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)
- 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.)
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- 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
- Creation and working of decorator
- Idea and practical example of generator, use of generator
- Concept and working of Iterator
- 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
- 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
- Threading, Multi-threading
- Memory management concept of python
- working of Multi tasking system
- Different os function with thread
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- 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
- 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 folder's details using OS
- What is Machine Learning
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- What is Time Series Analysis
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACF
- What is Exploratory Data Analysis
- EDA Techniques
- EDA Classification
- Univariate Non-graphical EDA
- Univariate Graphical EDA
- Multivariate Non-graphical EDA
- Multivariate Graphical EDA
- Heat Maps
- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
- Overview of Text Mining
- Need of Text Mining
- Natural Language Processing (NLP) in Text Mining
- Applications of Text Mining
- OS Module
- Reading, Writing to text and word files
- Setting the NLTK Environment
- Accessing the NLTK Corpora
- Tokenization
- Frequency Distribution
- Different Types of Tokenizers
- Bigrams, Trigrams & Ngrams
- Stemming
- Lemmatization
- Stopwords
- POS Tagging
- Named Entity Recognition
- Syntax Trees
- Chunking
- Chinking
- Context Free Grammars (CFG)
- Automating Text Paraphrasing
- Machine Learning: Brush Up
- Bag of Words
- Count Vectorizer
- Term Frequency (TF)
- Inverse Document Frequency (IDF)
- Introduction to TensorFlow 2.x
- Installing TensorFlow 2.x
- Defining Sequence model layers
- Activation Function
- Layer Types
- Model Compilation
- Model Optimizer
- Model Loss Function
- Model Training
- Digit Classification using Simple Neural Network in TensorFlow 2.x
- Improving the model
- Adding Hidden Layer
- Adding Dropout
- Using Adam Optimizer
- What is Deep Learning
- Curse of Dimensionality
- Machine Learning vs. Deep Learning
- Use cases of Deep Learning
- Human Brain vs. Neural Network
- What is Perceptron
- Learning Rate
- Epoch
- Batch Size
- Activation Function
- Single Layer Perceptron
- What is NN
- Types of NN
- Creation of simple neural network using tensorflow
- Image Classification Example
- What is Convolution
- Convolutional Layer Network
- Convolutional Layer
- Filtering
- ReLU Layer
- Pooling
- Data Flattening
- Fully Connected Layer
- Predicting a cat or a dog
- Saving and Loading a Model
- Face Detection using OpenCV
- Introduction to Vision
- Importance of Image Processing
- Image Processing Challenges – Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories
- Introduction to Image – Image Transformation, Image Processing Operations & Simple Point Operations
- Noise Reduction – Moving Average & 2D Moving Average
- Image Filtering – Linear & Gaussian Filtering
- Disadvantage of Correlation Filter
- Introduction to Convolution
- Boundary Effects – Zero, Wrap, Clamp & Mirror
- Image Sharpening
- Template Matching
- Edge Detection – Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector
- Effect of Noise
- Laplacian Filter
- Smoothing with Gaussian
- LOG Filter – Blob Detection
- Noise – Reduction using Salt & Pepper Noise using Gaussian Filter
- Nonlinear Filters
- Bilateral Filters
- Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding
- Image Sampling & Interpolation – Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling
- Image Interpolation – Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation
- Introduction to the dnn module
- Deep Learning Deployment Toolkit
- Use of DLDT with OpenCV4.0
- OpenVINO Toolkit
- Introduction
- Model Optimization of pre-trained models
- Inference Engine and Deployment process
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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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