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Data Science with Python Training in Dubai, United Arab Emirates

Learn to analyze data with Python in this Data Science with Python comprehensive course.

  • 42 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms

Online Classroom (Weekday)

Mar 30 - Apr 27 11:00 AM - 01:00 PM ( GST )

USD 2199

USD 649

Online Classroom (Weekday)

Mar 30 - Apr 27 01:00 PM - 03:00 PM ( GST )

USD 2199

USD 649

CITREP+ funding support is eligible for Singapore Citizens and Permanent Residents


Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.

Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analyzing, extracting information and making informed business decisions through big data.

This Data Science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.

Extensive hands-on labs and interview prep will help you land lucrative jobs.

What You Will Learn


There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

  • Those Interested in the field of data science
  • Those looking for a more robust, structured Python learning program
  • Those wanting to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researchers

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.


Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools & Technologies

Hands-on:  No hands-on

Learning Objectives:

In this module you will learn how to install Python distribution - Anaconda,  basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study


  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.

Topics Covered:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing 


Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.

You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.

Topics Covered:

  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA


  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.

Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.

Topics Covered:

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree


  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.

  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data. Work on a real- life Case Study with ARIMA.

Topics Covered:

  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • Case Study: Time Series Modeling on Stock Price


  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor


 Project to be selected by candidates.


Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science

From cloud kitchens to AI-powered real estate, every business has an online presence; generating millions of data every single day. At the same time companies need data to estimate and decide the future of a company. The work of a data scientist is to understand and codify data that will enable an organization to make comprehensive choices for their company. In such a situation, the demand for data scientists with excellent grasp of the medium becomes a necessary factor. There are other factors that play an important role for data science becoming a popular career choice in Dubai. They are:

  • The decision making of companies is highly data-driven.
  • There are around 1237 Tech startups in Dubai, including Network International, Property Finder, STARZ Play Arabia, Wadi, Intransa, Fetchr, Flemingo, etc. With the demand for professional data scientists not being fulfilled by the limited number of data scientists out there, the companies are paying a high salary to data scientists. 
  • Since data is being generated in high quantity, companies are shifting to data based decision making by using the raw data that is at their disposal. 

This leads to increased need for data scientists in every sector and makes data science a coveted career choice for employees.

Technical skills are essential in data science. Since, the work of data scientist is to classify, process and analyze data, they would need basic technical skills to adequately help a company make the best of the raw data available to them. Following are the main technical skills that are a must for anyone considering a job as a data scientist:

  • Python Coding: This is the most comprehensive and popularly used programming language. Python allows data scientists to create datasets and perform various operations on data sets.
  • R Programming: This is a variant of Python programming. Programming languages enable data scientists to understand and find patterns in raw data; making it essential to learn at least one programming language. 
  • Hadoop Platform: While not an absolute necessity, Hadoop platform is a preferred skill for a lot of data science projects. 
  • SQL database and coding: SQL is a platform that helps data scientists to access, communicate and work with data. With MySQL, data scientists can perform various operations on data easily without having technical skills.
  • Machine Learning and Artificial Intelligence: The potential Machine Learning and Artificial Intelligence skills required by data scientists are as follows:
    • Reinforcement Learning
    • Neural Network
    • Adversarial learning 
    • Decision trees
    • Machine Learning algorithms
    • Logistic regression etc.
  • Apache Spark: This is the most popular data sharing technology worldwide. This helps data science algorithms to run faster. Apache also helps in organizing and dissemination of data as well as handling complex unstructured data sets.
  • Data visualization: Data visualization tools like d3.js, Tableau, ggplot and matplotlib help in processing and formatting complex data sets for easy comprehension. This enables organizations to directly work with data since it is a graphical representation of the data that needs to be understood. The tools to grasp for data visualization are:
    1. Tabula
    2. Data Converter
    3. DataWrangler
    4. CSVKit
    5. Python and Pandas
    6. OpenRefine
    7. “R” packages
  • Unstructured data: Data Scientists have to work with unstructured data which is not labeled or classified into database values. These include videos, social media posts, audio samples, customer reviews, blog posts etc. Any data enthusiast should learn the following skills of cleaning tools
    • Trifecta
    • Paxata
    • Data Ladder
    • Alteryx
    • Win Pure
    • OpenRefine

Technical knowledge is not the only factor that determines the credibility of a Data Scientist. There are other factors that play a major role in how successful one will be in securing a Data Scientist job.

  • Asking ‘why’: Being constantly curious is an important quality to have in a data scientist as he/she will work with a large amount of data.
  • Clarity: Having a clear idea of why you are working with a particular data set and what can be achieved from working on it will determine your quality as a data scientist.
  • Creativity: Data science is all about having a drive to make your work environment efficient. Thus having the creativity to constantly reinvent methods of processing and analyzing data will be an added advantage.
  • Questioning judgments: Questioning what can and cannot work is the prerogative of the data scientist.
  • Versatility of skills: It is important to evolve with the changing work environment so that one can pick up on new programming trends. So one should have an open mind to learn new skills and have a good range of skills in their grasp.

There are many benefits to being in the job declared as the ‘Sexiest job of the 21st century’ by Harvard Business review in 2012:

  1. Highest paying job: Qualifying as a certified data scientist needs a lot of training and hard work, thus the pay is proportionate to the work put into it.
  2. Great bonuses: Though part of the salary, data scientists get huge bonuses including equity shares and signing perks.
  3. Privilege of becoming an educator: Becoming a data scientist requires a lot of knowledge. Thus by the time you become an expert you will probably have a Master’s or a PhD, which will help you get lecturer or a researcher opportunities.
  4. Networking: Being involved in the tech world by publishing research papers in international journals, or attending conferences will expand your interaction with people in the industry. 
  5. Security: Everyday there are new technologies coming up and disappearing without making any significant mark. This is not the case with data science. Being in data science field gives you a job security in the long term. 

Data Scientist Skills and Qualifications

  1. Analytical problem solving: To find a solution, one needs to have an analytical mind to understand the problem. In order to do that, one needs to be aware of all the strategies and have a clear perspective to reach the right solution. 
  2. Communication Skills: Collecting data and analyzing it is not the only responsibility of a data scientist. Unless you can communicate the customer analytics or business strategies to companies then your job is only half done.
  3. Industry knowledge: This is of great value if you want to be ahead of your competitors. Being up to date with the goings-on in the industry will help you understand what needs your attention and what you can discard. Being aware of what your global competitors are thinking and adapting them in your work will make you an asset in any company; bringing new opportunities. 

While you may become an expert in Data science, it is always preferred that you are up to date with the new developments in data science. For that you need to attend:

  • Bootcamps: Bootcamps are the best way to improve your Python programming skills. Bootcamps are held for 1 to 2 weeks or for 4-6 months, offering both theoretical knowledge as well as hands-on experience.
  • MOOC courses: These are virtual courses and provide excellent knowledge of latest trends in the industry. These courses are taught by experts helping you refine your implementation skills through assignments.
  • Projects: Projects are a great way to work on new solutions to already worked out problems depending on the restrictions of the projects. The more you work on projects, the better your analytical and problem solving skills will become.
  • Competitions: Attending competitions like Data Science Dojo or Kaggle Dubai, etc, improves your problem solving skills while giving you an idea of where you stand in relation to your peers.

Data Science can be really grasped through constant practice and by keeping yourself updated with new programming and preprocessing or analytic skills. Even after securing a job one should continue working on individual projects and enter competitions to brush up as well as have fun with the skills of data science.

Data science is still a developing area in Dubai, which makes it one of the most lucrative spaces to find jobs as data scientist. It is home to many exciting startups, such as Network International, Property Finder, STARZ Play Arabia, Wadi, Intransa, Fetchr, Flemingo, etc. Every new company or startup is looking for people with expertise in the field. Data Science provides the right information about the business and the customer experience which makes an expert in data science highly in-demand. 

The best way to improve your data science skills is to keep practicing and working your way through Data Science problems. Here, we have categorized different problems according to their difficulty level and your expertise level:

  • Beginner Level: Beginner level datasets do not need too much of programming knowledge. The basic knowledge of regression theory or classification algorithms will help in solving these data sets. The following data sets are great to work with while sharpening your skills.
    • Iris Data
    • Loan Prediction Data set
    • Heights and weights data
  • Intermediate Level: These are more complicated and require advanced identification skills and pattern recognition skills as they deal with larger mass of data. Some of the data sets in intermediate level are:
    • Movie set Data
    • Trip History Data
    • Siam Competition Data

  • Advanced Level: These datasets require knowledge of advanced topics like deep learning, neural networks, recommender systems. The data sets for this level are: 
    • Urban Sound Classification
    • Recommendation Engine Data
    • Vox Celebrity Data

How to Become a Data Scientist in Dubai, United Arab Emirates

The following points will guide you to become a successful data scientist:

  1. Acquire basic programming skills: One of the first steps towards becoming a data scientist is to learn a programming language. Python or R programming are the most preferred languages in the field of Data Science.
  2. Mathematics and statistics: Data science deals with data and this data can be in any form-numerical, textual or an image- which need to be compared and categorized. Having basic skills of algebra and statistics will make it easier to grasp the concepts of data science.
  3. Data visualization: The work of a data scientist is not just to understand data themselves but make it simple and coherent enough that non-experts can understand it perfectly. Visualization of data becomes an important aspect of data science as it is the end user who needs to understand the data generated more than the scientific aspect of data analysis. Having the ability to visualize patterns and common qualities will help the analyst to make sense of the data produced. 
  4. Deep Learning and ML: Having knowledge of deep learning and ML are a must for any data scientist. It is through the skills of deep learning and ML that data scientists analyze the data provided.
  5. Specialization: Many companies look for special skills like business or pricing. So it is important to know the sector one wants to work in and get the best experience in that so that your opportunities for applying to places go up. 

Some of the most successful companies in the world rely on data science for their business growth. Google, Amazon, Facebook or Twitter have the highest rate of employing data scientists. So, what should you do to get ahead of your peers? Below, listed, are the skill sets and steps you should take,

  1. Get a degree: Data scientists mostly consist of Master’s or PhD degree holders. Hence, it is important to start preparing, reading and practicing as early as you can. You could get into numerous programs online or offline, or get yourself a degree on basics of mathematics and algebra.
  2. Handling large quantity of data: Handling unstructured data is essentially the job of a data scientist. How to categorize the infinite number of data getting stored is the most important responsibility of a data scientist. Working on data sets and projects can improve one’s eye for useful data. 
  3. Software and techniques to master: Python, R and Hadoop are important tools to stay accustomed with as a Data Scientist. More than 53% data scientists are fluent in both R and Python programming.  Being accustomed to using these will kick-start your data science career.

Below are some benefits of getting a degree:

  1. Networking: Interacting with your peer group will increase your clarity and you will find networking opportunities. Having acquaintances in the industry always gives people an edge. 
  2. Structured learning: Having a schedule for your curriculum will not only provide a holistic idea about the discipline, it will also help in maintaining timelines.
  3. Internships: Getting hands on experience by doing internships can be very helpful and provide you with an idea about the workload you will be expected to take up. 
  4. Appropriate academic degrees and qualification: While having a degree from a prestigious university does provide an advantage to your career, it is also important that you have appropriate degrees. 

The need for a master’s degree in Data Science depends on the degree one has pursued before. The necessity of a Master’s degree depends on the following points mentioned below. Score yourself according to the factors mentioned, if you score more than 6 points it is advisable that you undertake a master’s degree.

  • You have a strong STEM (Science/Technology/Engineering/Management) background: 0 points.
  • You have a weak STEM background (Biochemistry/Biology/Economics or other such degrees): 2 points. 
  • You come from a non-STEM background: 5 points
  • You have less than 1 year experience of working with Python programming: 3 points
  • You have never had a job which required you to code on a regular basis: 3 points
  • You feel you are not good at independent learning: 4points
  • You do not understand when it is said that this scorecard is a regression algorithm: 1 point.

Knowledge of programming is perhaps the most important and fundamental skill that an aspiring data scientist must possess. Some of the other reasons why knowledge in programming is required include the following: 

  • Data sets: Data sets are basically a collection of data. Algorithms are written to work on these data sets, therefore it is very essential to have a command over one or more programming languages. Some of these programming languages are as follows:
    • R
    • Python
    • Scala
    • Julia
    • TensorFlow
    • Java
  • Statistics: Statistics is important for Data analysis. To recognize a pattern and work on them requires a good knowledge of statistics. A concrete understanding of multivariable calculus and linear algebra is essential for a data scientist.
  • Framework: The most recommended framework for Data Science is Hadoop which is an open-source software framework and is heavily preferred in several data science projects for processing of large data sets. One important feature of it is that it can store unstructured data such as text, images, and video. The benefits of Hadoop are features like flexibility, scalability, fault tolerance, and low cost which makes it a preferable choice for data scientists.

Data Scientist Jobs in Dubai, United Arab Emirates

Here are the steps that you must follow in order to become a top-notch Data Scientist:

  1. Getting started: Choose a language that you are most comfortable with. The most commonly used programming languages in Data Science are Python and R language.
  2. Mathematics: You need to have a good knowledge of mathematics and statistics as the responsibilities of a data scientist entails making sense of the raw data, finding patterns in the data and then representing them.
  3. Libraries: Get skilled in several libraries like Pandas, Matplotlib, SciPy, Scikit-learn, NumPy, ggplot2, etc., these are used to preprocess the data, plot the structured data and apply machine learning algorithms to the data
  4. Data visualization: Visualizing the data is very important as you will be required to find sense in the raw data provided to you, find relevant patterns and make it simple for the non-technical members of the team.  
  5. Data preprocessing: Next step is to preprocess the data so that it is ready for the analysis. It can be done using variable selection and feature engineering.  
  6. ML and Deep learning: You need to have a sound knowledge on topics like CNN, RNN, Neural networks, etc. Deep learning algorithms are used while dealing with a huge set of data.
  7. Natural Language processing: Natural language processing involves processing and classification of textual data. Every data scientist must be an expert in NLP.  
  8. Polishing skills: You can exhibit your data science skills in competitions like Kaggle. You can also explore the field by experimenting and creating your own projects. 

The following ways might help you prepare before the day of the interview.

  • Study: Re-read whatever you have learnt till now. There are few things you could brush up on:
    • Probability
    • Statistics
    • Statistical models
    • Machine Learning
    • Understanding of neural networks
  • Meetups and Conferences: Going to tech summits or developer meetups will acquaint you with the people who could one day become your colleagues. This is a good way to do some networking.
  • Competitions: Competitions are the best platforms to test your skills. Taking up projects to work on from Kaggle or GitHub would help polish your skills.
  • Referral: Having good referrals is considered one of the most important parts of a job interview. You should always keep your LinkedIn profile updated. 
  • Know your Employer: Always research on the organization you are going into. Having an idea of the type of company and values the company has will give you a perspective to your interview.
  • Interview: Once you feel that you are ready for taking an interview, take one. Be comfortable and learn from your experience. Think of where you went wrong and how you could have answered the question that you were not prepared for during the interview. 

Making data easy to infer from is the job of a data scientist. Finding patterns among structured and unstructured data, and analyzing them for the purpose of business growth will be a significant responsibility of a data scientist. 

 Data Scientist Roles & Responsibilities: 

  1. Classifying structured and unstructured data through pattern recognition and creating database.
  2. Finding data that is relevant to the business and can be profitable from among the vast quantities of data.
  3. Develop Machine Learning technologies, programs and tools which will make accurate analysis of the data.
  4. Statistical analysis of appropriate data for predicting future developments of a company is also expected of a data scientist.

Data Science is the hottest job of 21st century and number one profession in 2019. Due to the extreme demand for data scientists and the limited number of experts in the field, data scientists earn at least 36% higher than predictive analytics professionals. The salary of a data scientist depends on two factors:

  • Nature of company
    • Startups- high pay
    • Public- medium pay
    • Government and education sector- lowest pay
  • Roles and Responsibilities
    • Data Scientist: 30,000 to 45,000 Dirham per month
    • Data Analyst: 25,000-35,000 Dirham per month
    • Database Administrator: 50,000 to 70,000 Dirham per month

A data scientist has the most unique position in a company. He/She will need to have an aptitude for mathematics, understand computer science and at the same time stay aware of current trends. A data scientist not only analyzes data but finds the relevant ones and directs the future of a company by predicting future outcomes. Thus there are various roles and responsibilities of a data scientist. The following responsibilities are a part of a data scientist’s career graph:

  • Business Intelligence Analyst: Anyone in this position is expected to analyze the available data to understand the business and marketing trends of the industry his/her company is part of.
  • Data Mining Engineer: An engineer in data science has the task of analyzing data for the company as well as other third parties. Not only that, engineers are expected to optimize data analysis process by developing sophisticated algorithms. 
  • Data Architect: A Data Architect’s work is to make the data sources more approachable. He/She works alongside developers, system designers to integrate and protect data while finding ways of centralizing it making it more accessible. The responsibilities of a Data Architect include:
    • Data Cleaning
    • ETL working
    • Data Warehousing
    • Elastic working and functioning
  • Data Scientist: The data scientist works as an interpreter and idea creator by working with sets of data that correspond with particular business ventures and predicts the efficacy of it by developing hypotheses and comparing similar data.
  • Senior Data Scientist: A senior data scientist is expected to work with data in order to predict the future of a company. He/She should create projects and develop systems in the present with an eye towards the future so that the future conditions of a company can be predicted.

Some renowned associations and groups of data scientists are:

  • Data Science UAE
  • Dubai Data Science
  • Innosoft Gulf - Big Data and Artificial Intelligence
  • Big Data, Dubai
  • UAE Big Data Group

There are various ways one can look for potential employees:

  1. Through Data Science conference
  2. Online platforms like LinkedIn
  3. Social gatherings like Meetup
  4. Paper presentations
  5. Following influencers in the field of Data Science

There are several career options for a data scientist in Dubai, UAE. These include – 

  1. Data Scientist
  2. Data Architect
  3. Data Administrator
  4. Data Analyst
  5. Business Analyst
  6. Marketing Analyst
  7. Data/Analytics Manager
  8. Business Intelligence Manager
  9. IoT Specialist

Below are the key points on which every data scientist is evaluated for being considered as a potential employee.

  • Education: Since data science requires sophisticated level of interpretation, having higher level education is always a criteria. Data scientists are considered to hold the most number of PhDs. Even getting certified can also help in getting employment.
  • Programming: Programming is a crucial part of data science. Being well versed in R and Python programming languages are a must for any data scientist as most of the work is done through these.
  • Machine Learning: It is ML and deep learning that analyzes data to find patterns and relationships after they have been prepared. Machine learning is imperative to any data science projects. ML skills that should be mastered are:
    • Random Forest
    • Logistic Regression
    • Clustering
    • K Nearest Neighbor
  • Projects: Companies look for hands-on experience of data scientists. Thus projects are a good way of providing an understanding of your capabilities and also add to your resume.

Data Science with Python Dubai, United Arab Emirates

  • Python is a versatile multi faceted programming language:
  • Python is the most simple and readable programming language that instantly attracts data scientists. It comes with appropriate analytic libraries and tools that are ideal for the kind of work done in data science.
  • The diversity of resources available on Python makes it a safe option for data scientists.
  • Another advantage of using Python is the availability of a community of developers using the same programming language. Python being the most popular programming language, the number of people working on it is high.

Data Science is a huge field which requires working with a large number of libraries. Finding the right programming language to master is, therefore, important for efficient working with all the libraries-

  1. R programming: The only challenge of R is its steep learning curve, but it is an important language for various reasons. It has a huge open-source community that provides numerous high quality open-source packages for R. It boasts of smooth handling of matrix operations and has large statistical functions. It has included with it ggplot2 that enables data visualization.
  2. Python: With lesser packages than R, Python is still considered to be popular with data scientists. The reasons for that is-
    • Libraries like pandas, scikit-learn and tensorflow equip Python to provide most library needs for data science purposes.
    • It is very easy to use and operate.
    • It has an open-source community that is considered one of the largest in the world.
  3. SQL: Working on relational databases, Structured Query Language has-
    • Readable syntax
    • Efficiency in updating, manipulating and querying data for relational databases
  4. Java: One of the oldest programming languages, Java has limited libraries limiting its potential. Nevertheless it has some advantages.
    • Systems coded with Java at the backend makes it easier to integrate data science projects with it making it a compatible option.
    • It is a high performance, general purpose, compiled language
  5. Scala: Working on JVM, it is considered rather complicated. But it does have some advantages-
    • Running on JVM, Scala can run on Java as well.
    • Used alongside Apache Spark it enables high performance computing cluster.

The following are the steps to downloading Python 3 for Windows:

  • Download and setup: Go to the download page and setup your python on your windows via GUI installer. While installing, select the checkbox at the bottom asking you to add Python 3.x to PATH, which is your classpath and will allow you to use python’s functionalities from terminal.

Alternatively, you can also install python via Anaconda as well. Check if python is installed by running the following command, you will be shown the version installed:

python --version

  • Update and install setuptools and pip: Use below command to install and update 2 of most crucial libraries (3rd party):

python -m pip install -U pip

Note: You can install virtualenv to create isolated python environments and pipenv, which is a python dependency manager.

You can simply install python 3 from their official website through a .dmg package, but we recommend using Homebrew to install python as well as its dependencies. To install python 3 on Mac OS X, just follow the below steps:

  • Install xcode: To install brew, you need Apple’s Xcode package, so start with the following command and follow through it: 

$ xcode-select --install

  • Install brew: Install Homebrew, a package manager for Apple, using the following command: 

/usr/bin/ruby -e "$(curl -fsSL"Confirm if it is installed by typing: brew doctor

  • Install python 3: To install the latest version of python, use: 

brew install python

  • To confirm its version, use: python --version

You should also install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions.

reviews on our popular courses

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The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, KnowledgeHut!

Ong Chu Feng

Data Analyst
Attended Data Science with Python Certification workshop in January 2020
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The course which I took from Knowledgehut was very useful and helped me to achieve my goal. The course was designed with advanced concepts and the tasks during the course given by the trainer helped me to step up in my career. I loved the way the technical and sales team handled everything. The course I took is worth the money.

Rosabelle Artuso

.NET Developer
Attended PMP® Certification workshop in May 2018
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Overall, the training session at KnowledgeHut was a great experience. I learnt many things. I especially appreciate the fact that KnowledgeHut offers so many modes of learning and I was able to choose what suited me best. My trainer covered all the topics with live examples. I'm glad that I invested in this training.

Lauritz Behan

Computer Network Architect.
Attended PMP® Certification workshop in May 2018
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Everything from the course structure to the trainer and training venue was excellent. The curriculum was extensive and gave me a full understanding of the topic. This training has been a very good investment for me.

Jules Furno

Cloud Software and Network Engineer
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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The skills I gained from KnowledgeHut's training session has helped me become a better manager. I learned not just technical skills but even people skills. I must say the course helped in my overall development. Thank you KnowledgeHut.

Astrid Corduas

Senior Web Administrator
Attended PMP® Certification workshop in May 2018
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KnowledgeHut is a great platform for beginners as well as experienced professionals who want to get into the data science field. Trainers are well experienced and participants are given detailed ideas and concepts.

Merralee Heiland

Software Developer.
Attended PMP® Certification workshop in May 2018
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Knowledgehut is among the best training providers in the market with highly qualified and experienced trainers. The course covered all the topics with live examples. Overall the training session was a great experience.

Garek Bavaro

Information Systems Manager
Attended Agile and Scrum workshop in May 2018
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I really enjoyed the training session and am extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.

Tilly Grigoletto

Solutions Architect.
Attended Agile and Scrum workshop in May 2018


The Course

Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

 Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

  • Get advanced knowledge of data science and how to use them in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like Python

By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.

Tools and Technologies used for this course are

  • Python
  • MS Excel

There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Python and data science experts who have years of industry experience. 

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

Have More Questions?

Data Science with Python Certification Course in Dubai

A glittering city seemingly blessed by the Gods themselves, Dubai is the stuff of dreams. A futuristic city with deep rooted traditions, it has emerged as a global business hub. While oil was a major factor in the early development, today the city has a diversified economy with sectors like shipping, banking, finance, and real estate offering considerable employment. The prolific architecture in Dubai is evident in the modern and ancient Islamic architecture. The buildings of Burj Al Arab and Burj Khalifa are a reflection of the city?s success and economic supremacy. Shopping of course is Dubai?s pi?ce de r?sistance, and tourists flock here to participate in the Dubai shopping festival. With so many shopping centres, boutiques and malls, its no wonder that Dubai has been called the ?shopping capital of the Middle East?. For a glittering career in Dubai you can pursue one of KnowledgeHut?s several courses including PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache Hadoop, SAFe Practitioner, Agile User Stories, CASQ, CMMI-DEV and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.