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 analysing, 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 an interview prep will help you land lucrative jobs.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
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.
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.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
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.
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.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
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.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
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).
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:
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:
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.
There are many benefits to being in the job declared as the ‘Sexiest job of the 21st century’ by Harvard Business review in 2012:
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:
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:
The following points will guide you to become a successful data scientist:
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,
Below are some benefits of getting a degree:
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.
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:
Here are the steps that you must follow in order to become a top-notch Data Scientist:
The following ways might help you prepare before the day of 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:
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:
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:
There are various ways one can look for potential employees:
There are several career options for a data scientist in Dubai, UAE. These include –
Below are the key points on which every data scientist is evaluated for being considered as a potential employee.
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-
The following are the steps to downloading Python 3 for Windows:
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 -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:
$ xcode-select --install
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"Confirm if it is installed by typing: brew doctor
brew install python
You should also install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions.
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Trainer at KnowledgeHut made sure to address all my doubts clearly. I was really impressed with the training and I was able to learn a lot of new things. It was a great platform to learn.
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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. Payscale.com 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.
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
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
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.
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.
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