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.
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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).
In the Harvard Business Review of 2012, Data Scientist has been dubbed as the sexiest job of the 21st Century. Data is collected from companies like Google and Facebook and is sold to advertisement companies which earn crazy profits. How do you think they know you like coffee or tea? How does Amazon recommend you the products you were just thinking to purchase? The answer to these questions is data.
Cape Town is one of the most advanced cities in Africa. It is home to several leading companies such as Luno, Rogerwilco, The Skills Mine, OfferZen, E-Merge, etc. and universities that offer major courses in data science.
These are the major reasons why data science is so popular:
This indicates that Data Scientists are in huge demand these days. This work profile is important from the company’s perspective as well as that of the employees.
Technical skills are important for pursuing a career in Data Science. Cape Town is home to leading universities, including the University of the Western Cape, University of Cape Town, Department of CS, etc. The journey to becoming a data scientist is a tough and challenging one. A Data Scientist should have these skills to excel in this field:
A good Data Scientist should have these top 5 behavioural traits to have a successful career in the field of Data Science:
As ‘Data Scientist’ has been given the award of being “the Sexiest Job of the 21st Century”, it is natural that working as a Data Scientist professional will have numerous benefits all around the world and not just in Cape Town. Here is a list of 5 proven benefits of being a Data Scientist:
A Data Scientist should have good business skills to sustain in the job market. These essential skills are applicable everywhere irrespective of the location. Here is a list of 4 must have business skills every Data Scientist must have:
Every profession requires skills to brush up so that professionals working in that field remain up to date and informed. Here is a list of the 5 best ways to brush up your Data Science Skills to get a Data Scientist job:
Data rules the world today. Everything from your medical diagnosis, investment in the stock market to your browser history is data. Each of these types of Data is being collected and monitored closely to find patterns. Companies and organizations are collecting personal information, professional information as well as other data for their own benefits. However, this collection of data also results in the improvement of customer service.
This city has several huge companies which offer data science jobs such as Luno, Rogerwilco, The Skills Mine, OfferZen, E-Merge etc.
Companies offers various types of Data Science jobs depending upon the work they do and the people they cater to:
The practice is the best way to learn, understand and master the art of Data Science. One can only achieve mastery in this field by working their way through the problems created while analyzing the data. It is important to be as close to the real problem faced in Data Science to get the most out of the learning experience. This is a list of Data Science problems, which have been categorized into three levels- Beginner, Intermediate and Advance – according to the difficulty levels of the problems mentioned:
These steps will guide you in the direction to becoming a top-notch Data Scientist:
As previously mentioned, the job of a Data Scientist has been given the title of “The Sexiest Job of the 21st Century” by none other than Harvard Business Review. Cape Town offers a great opportunity for aspiring data scientists to learn various essential skills through the various eminent universities it has such as the University of Cape Town, Department of CS, University of the Western Cape etc. How should one prepare for a career in Data Science? Here is a list of some skill sets and steps required to be a successful Data Scientist. One must also remember that this list will help you everywhere not just in Cape Town:
As mentioned earlier, a degree or a certificate in Data Science will open up new and better opportunities for any prospective Data Scientists. Statistically, approximately 88% of data scientists hold a Master’s degree. Along with this 46% of all data, scientists are PhD degree holders.
University of Cape Town, Department of CS, University of the Western Cape etc. are some of the elite universities of the city which offer advanced degrees in the field of data science.
A degree is an essential part of Data Science because of the following –
Entry-level positions may not require any Master’s degree but senior positions will definitely have a requirement of a Masters degree. That is why living in Cape Town serves as an advantage due to the availability of several renowned universities the city has. Here is a way to determine whether or not you would need a Master’s degree in the field of Data Science. If the total number that comes up if greater than 6, then it is advisable to go for a relevant Master’s degree.
It is imperative that an aspiring Data Scientist possess knowledge of programming. It is perhaps one of the most fundamental and vital skills required in this field. Here are some reasons why programming is a skill every Data Scientist should possess:
Given below is a list of steps that should be followed in a sequence to get a job in Data Science:
These steps are imperative if you want to become a successful data scientist no matter which place you’re in. Follow the below steps to increase your chances of getting a job as a Data Scientist:
The roles and responsibilities of a Data Scientist include discovering patterns and relationships between different sets of data. Along with this, they are also responsible for inferencing information from unstructured as well as structured data pools so as to meet the goals and needs of an organization.
Tons of data gets generated everyday and keeping up with this huge amount of data is a tedious task. The role of a Data Scientist becomes even more important because of this. Data is one of the most important assets of any company which deals directly with the consumers. It helps in establishing patterns and ideas which in turn are useful for the advancement of an organization. A Data Scientist is responsible for extracting relevant information from the data pools and using it for the benefit of the company.
Data Scientist Roles & Responsibilities:
A data scientist has been declared as the hottest job of the 21st century. Cape Town is one of the most advanced cities of Africa. This city has several huge companies which offer data science jobs such as Luno, Rogerwilco, The Skills Mine, OfferZen, E-Merge etc. There is a huge demand for Data Scientists but the supply of professional and well-trained people in the field is low. This ensures that the salaries of Data Scientists are higher than professionals in other fields.
There are two things which help in determining the pay scale of Data Scientists:
A Data Scientist should have the ability of a mathematician, a computer scientist as well as a trend spotter. The roles and responsibilities of a Data Scientist includes organizing and handling large amounts of data, extracting the relevant data and analyzing the extracted data to predict the outcomes.
Career Path of a Data Scientist is explained here -
Business Intelligence Analyst: The job of a Business Intelligence Analyst is to study the market trends and figuring out the popular business trends. This is done by organizing the extracted data and analyzing the data closely to find the patterns and trends. This helps in getting a clear picture of the business trends.
Data Mining Engineer: A Data Mining Engineer examines the data that is relevant to not only the business/company he/she is working for but also the third-party clients with invested interests. Along with this, the roles and responsibilities of a Data Mining Engineer also includes creating algorithms which help in proper analysis of the data.
Data Architect: The main role of a Data Architect is to work with system designers and developers to develop blueprints. These blueprints are used in database management systems to sort, filter, integrate, protect, maintain and analyze the data. It also helps in centralizing the data sources.
Data Scientist: A Data Scientist is responsible for the analysis of business cases. Along with this, the main responsibilities of a Data Scientist include the development of data understanding, development of data hypotheses and exploring pattern in the data. Development of algorithms and systems for the advancement of interests of the business also come under the responsibilities of a Data Scientist.
Senior Data Scientist: The role and responsibilities of a Senior Data Scientist include the anticipation of Business needs in the future. The Senior Data Scientist is also responsible for shaping future projects for the business based on data predictions and analyses.
Below are the top professional organizations for data scientists in Cape Town –
Networking is the key to get hired in a top-notch company. Building contacts and networking can be done through the following channels –
Top 8 Data Science Career Opportunities in 2019 in Cape Town are -
Cape Town has several huge companies which offer data science jobs such as E-Merge, Luno, OfferZen, Rogerwilco, The Skills Mine, etc. which offer high salaries and demand deep mastery in the field -
Choosing an appropriate programming language is important in the field of Data Science. As the field is huge and involves numerous libraries, it is imperative to use different languages which have different purposes.
R: R is a programming language which focuses on the analysis of data. It is a preferred tool while working with any kind of data which requires extensive analysis. Data Scientists should have a comprehensive knowledge of an analytical tool such as R Programming. The programming language makes it easier to handle large amounts of data. R offers statistical techniques such as classical statistical tests, linear modelling, non-linear modelling, classification, clustering etc. to make data handling, data storage, calculation and data analysis easier. R offers high quality open-source packages, loads of statistical functions and great visualization tools.
PYTHON: Python is one of the most famous as well as the most commonly used programming language. It is a crucial skill to have in the field of Data Science. It is a general purpose, high-level programming language. The language was developed to emphasize on the readability of codes and to make the syntax simpler to read and write. As Python offers versatility and simplicity, processing of data becomes simpler and easier. Various formats of data are accepted by Python which makes the integration between these types of data easier and multiple operations can be performed by professionals to achieve the required results. Along with this, datasets can be created, and codes can be written to store and do calculations.
SQL: SQL which stands for Structured Query Language, is a programming language which helps in communicating with a database. It is a domain-specific language and helps in accessing, communicating and working on data easier. It is designed to manage and process large amounts of data. SQL statements can also be used to update and retrieve from any database. By using this programming language, a Data Scientist can gain insights into the formation as well as the structure of a database.
JAVA: Even though, Java has a smaller number of libraries when compared to other programming languages used in Data Science it has several advantages. Java is compatible with most systems as a majority of them are coded in Java. This makes it easier to integrate into the system. Java is a general purpose, compiled and high performing programming language.
SCALA: Scala is a preferred language among Data Scientists as it runs on JVM. Even though this gives it a complex structure, it’s high performing cluster computing covers up for the complexity. An added advantage of Scala is that it can run on Java as well.
These are the steps to install Python 3 on Windows:
To install Python on MAC OS X, download the .dmg package and install it. It is recommended to use Homebrew to install python by following these steps:
Type ‘brew doctor’ to confirm installation,
Alternatively, virtualenv can also be used to install Python. This will help in creating isolated places where different projects can be run separately.
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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
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