Data Science with Python Training in Cape Town, South Africa

Get hands-on Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit.
  • Create robust predictive models with advanced statistics.
  • Leverage hypothesis testing and inferential statistics for sound decision-making.
  • 250,000 + Professionals Trained
  • 55,000 + Programmers upskilled
  • 70 + Countries and counting

Grow your Data Science skills

This four-week course takes you from the fundamentals of Data Science to an advanced level. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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Highlights

  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to Advanced Learning

  • Code Reviews by Professionals

Why Become a Data Scientist?

Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need.

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Not sure how to get started? Let our Learning Advisor help you.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Exclusive Post-Training Sessions

Practical one-to-one guidance from mentors: project review and evaluation, guidance on work challenges.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Prerequisites

Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend this course.
  • Elementary programming knowledge will be useful.

Who should attend this course?

Anyone interested in the field of data science

Anyone looking for a more robust, structured Python learning program

Anyone looking to use Python for effective analysis of large datasets

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

1

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

2

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

3

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

4

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

5

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

6

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

7

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Learning objectives

Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


Topics

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

Learning objectives

The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization

Topics

  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study

Hands-on

  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives

In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value

Topics

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

Hands-on

  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives

Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.

Topics

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

Hands-on

  • With attributes describing various aspect of residential homes for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives

Take your advanced statistics and predictive modelling skills to the next level in this advanced module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression & classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study

Topics

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

Hands-on

  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives

All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

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

Topics

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

Hands-on

  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing 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.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives

This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


Hands-on

  • Project to be selected by candidates.

Frequently Asked Questions

Data Science with Python Training

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Data Science with Python course is ideal for:

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

There are no prerequisites for attending this course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a course completion certificate from KnowledgeHut.

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at support@knowledgehut.com and we will be happy to get back to you.

What Learners Are Saying

Ong Chu Feng

Ong Chu Feng

Data Analyst

4/5

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 un View More

Attended Data Science with Python Certification workshop in January 2020

Merralee Heiland

Merralee Heiland

Software Developer.

5/5

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 conce View More

Attended PMP® Certification workshop in April 2020

Jules Furno

Jules Furno

Cloud Software and Network Engineer

5/5

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 fo View More

Attended Certified ScrumMaster (CSM)® workshop in June 2020

Mirelle Takata

Mirelle Takata

Network Systems Administrator

5/5

My special thanks to the trainer for his dedication and patience. I learned many things from him. I would also thank the support team for their help. It was well-organised, great work Knowledgehut team!

Attended Certified ScrumMaster (CSM)® workshop in July 2020

Steffen Grigoletto

Steffen Grigoletto

Senior Database Administrator

5/5

Everything was well organized. I would definitely refer their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussio View More

Attended PMP® Certification workshop in April 2020

Garek Bavaro

Garek Bavaro

Information Systems Manager

5/5

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 exp View More

Attended Agile and Scrum workshop in February 2020

Archibold Corduas

Archibold Corduas

Senior Web Administrator

5/5

The teaching methods followed by Knowledgehut is really unique. The best thing is that I missed a few of the topics, and even then the trainer took the pain of taking me through those topics in the next session View More

Attended Certified ScrumMaster (CSM)® workshop in May 2020

Meg Gomes casseres

Meg Gomes casseres

Database Administrator.

5/5

The 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. I would certainly recommend it to my team.

Attended PMP® Certification workshop in January 2020

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Data Science with Python

What is Data Science

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:

  1. Decision making which includes Data Science is in popular demand.
  2. There is a lack of well-trained data scientists in the market. This leads to an increase in the demand of professionals trained in data science which in turn results in one of the highest salaries in the tech world.
  3. Data Analysis is an important step after the collection of Data which has been done at an exceptionally high rate. The data that has been collected requires an equally active data analysis system. Crucial marketing decisions by companies are taken after studying the raw data analysis done by Data Scientists. 

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:

  1. Python Coding
  2. R Programming
  3. SQL Database and Coding
  4. Data Visualization
  5. Hadoop Platform
  6. Machine Learning and Artificial Intelligence
  7. Apache Spark
  8. Unstructured Data
  1. PYTHON CODING: 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 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. It is essential for a Data Scientist to accurately analyze any data using Python.
  2. R PROGRAMMING: 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. Professionals who have knowledge of R Programming have an edge over others in the field of Data Science.
  3. SQL DATABASE CODING: 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.
  4. DATA VISUALIZATION: Data Visualization is another tool, like SQL, which gives Data Scientists the opportunity to work directly and communicate with the database. Tools like d3.js, Tableau, ggplot and matplotlib are used by Data Scientists to visualize the data. By using these tools, a data scientist can perform complex data processes and convert the data sets into formats which are less complicated, accessible and easy to understand and comprehend. Data Visualization offers opportunities which allow organizations to quickly grasp insights on a particular set of data. It also offers opportunities which help Data Scientists in deciding the outcomes as well as enables them to act on new outcomes obtained from extensive analysis.
  5. HADOOP PLATFORM: Hadoop Platform is not a necessary tool required for Data Science, but it is a skill a good Data Scientist will have. This is a platform which might not be applicable for all projects but has great significance wherever required. Apache Hadoop has a collection of multiple open-source software that help in solving problems related to large amounts of data. A study on LinkedIn shows that Hadoop is considered a great skill to have for professionals working in the field of Data Science.
  6. MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE: In recent years, Machine Learning and Artificial Intelligence have become a requirement to pursue a career in Data Science. Machine Learning is the application of Artificial Intelligence to make working and processing data easier and hassle-free. It is a prerequisite which all organizations expect their prospective Data Scientists to fulfil before joining their team. Professionals in this field, who are familiar with Machine Learning and Artificial Intelligence, should have knowledge of the following:
    • Machine Learning Algorithms
    • Neural Network
    • Reinforcement Learning
    • Logistic Regression
    • Decision Trees
    • Adversarial Learning
  7. APACHE SPARK: Apache Spark is one of the most popular and accessible data sharing tools in the world. Hadoop and Apache Spark are quite similar in the role they play in the world of Data Science. The difference lies in the way the two technologies work. On the one hand, Apache Spark uses system memory to process data and on the other hand, Hadoop uses the disk to read, write and maintain the data, which makes it slower. Apache Spark is faster and more efficient and is preferred by professionals as it helps in running the data science algorithms faster. The technology is also useful when large, complex and unstructured data has to be broken down, read and analyzed. One of the main benefits of using Apache Spark is that it helps in retaining data in case of any data loss. It protects the interests of the organization as well as their employees.
  8. UNSTRUCTURED DATA: Unstructured Data is a very common thing in the field of Data Scientist. A good Data Scientist should be knowledgeable and aware enough to handle such unstructured data. Every professional should be familiar with the process and procedure to handle data which has not been labelled and categorized. Videos, Audios, Samples, Customer Reviews, Social Media Posts, Blog Posts are some basic examples of unstructured data which a Data Scientist encounters on a daily basis.

A good Data Scientist should have these top 5 behavioural traits to have a successful career in the field of Data Science:

  • Curiosity: As a Data Scientist deals with massive amounts of data, it is imperative to have a natural curiosity and hunger for knowledge. Data Analysis and Problem Solving requires curiosity along with technical skills. Lack of curiosity will make the job of a Data Scientist boring and monotonous.
  • Clarity: It is important for a Data Scientist to ask the questions “Why?” and “What?”. This will help in making clear and informed decisions. Whether it is data analysis or writing codes, it is necessary for professionals to be clear about what to do and how to do it.
  • Creativity: The job of a Data Scientist is as creative as it is technical. An equal balance between technology and creativity must be maintained to ensure that any data is being handled carefully. Data Scientists must find innovative and creative ways to visualize data, develop new tools and methods etc. Finding creative ways to do a task is a major behavioural trait of a successful Data Scientist.
  • Scepticism: Although creativity is important in this field, it must be kept in check. It is important to maintain a balance between creativity and rationality. Scepticism is a trait which helps keep Data Scientists on the right track without being distracted and carried away with creativity.

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:

  • HIGH PAY: No matter what field a person is in, a high paying job is always an advantage. Everyone wants a job which pays high enough to afford life’ luxuries. As a Data Scientist, where the qualification bar is set quite high, a nice salary can be expected by prospective professionals. As the demand of Data Science professionals is high in the market and the supply of such professionals is limited, this job is one of the highest paying ones in the IT industry. The average pay in Cape Town is R 596,400 per year.
  • GOOD BONUS: As Data Scientists are in high demand, benefits, other than the basic salary package, are also high. Data Scientists get great bonuses and other perks may also include equity shares.
  • EDUCATION: To get a job as a Data Scientist a person will require at least a Masters in the field. Some people may also hold a PhD in Data Science which will open better opportunities. With such a high level of education, a Data Scientist will get good offers from corporate organization, colleges and universities as well as government institutions.
  • MOBILITY: As Data Science requires high level technology, most companies dealing with data are located in developed countries. This would give Data Science professionals opportunities to work in developed countries, get a great salary and raise their standard of living.
  • NETWORK: As a newly established field, the worldwide community of Data Science, Machine Learning and Artificial Intelligence is quite small compared to fields which have been present in the market for decades. This gives Data Science Professionals opportunities to network with people in their field. The networking can be used for referral purposes as well.

Data Scientist Skills & Qualifications

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:

  1. Analytical Problem Solving
  2. Communication Skills
  3. Intellectual Curiosity
  4. Industry Knowledge

  • ANALYTICAL PROBLEM SOLVING: Data Science is all about data analysis and problem solving. Therefore, it is necessary for a Data Scientist to have good analytical problem-solving skills. Professionals must first understand and analyze the problem and then analytically find a solution to the problem. To do this, an analytical and logical approach along with perspective and awareness of the industry and strategies is needed.
  • COMMUNICATION SKILLS: One of the key responsibilities of Data Scientists is to communicate effectively. This skill is important because Data Scientists are required to communicate customer analytics and deep business strategies to companies.
  • INTELLECTUAL CURIOSITY: As mentioned before, a successful Data Scientist is always curious. It is the curiosity which drives the professional to find the answers to questions such as “Why?”, “What?” and “How?”. Along with this curiosity, to be a successful Data Scientist, one must also have to enthusiasm to find answers and deliver results.
  • INDUSTRY KNOWLEDGE: This is one of the most important things one must have to become a successful Data Scientist. To get a clear idea of what needs to be done, it is imperative to have deep industry knowledge. Without this, working in this field will be difficult and growth in the career will be stagnated.

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:

  • BOOT CAMPS: Python boot camps are the quickest and easiest way to brush up your knowledge of the programming language. These boot camps are of a duration of 4 to 5 days and cover all the basics of the language. Along with this, the course curriculum of these boot camps includes both theoretical as well as practical knowledge. Boot Camps are the best way to update your knowledge within a limited time span.
  • MOOC COURSES: Online courses and certifications are a new way to learn these days. All types of courses related to Data Science are available on the internet. Some of these courses are paid and some are free. The instructors of these courses are generally industry experts who have ample experience and knowledge about the subject. The online courses have assignments and tests along with tutorials to help test and judge the learning progress of the learner. 
  • CERTIFICATIONS: Certifications are short term courses which offer additional skills related to the field. Certifications are the best way to add significance to your CV. Some famous and recognized Data Science Certificate courses include:
  • Applied AI with Deep Learning, IBM Watson IoT Data Science Certification
  • Cloudera Certified Professional: CCP Data Engineer
  • Cloudera Certified Associate: Data Analyst
  • LIVE PROJECTS: Live Projects are the best way to experience the practical side of any field. It gives you the opportunity to apply your theoretical knowledge in real-life situations. As these projects are ongoing and real, every person working on it becomes accountable for the work done. Live Projects are the best way to learn the workings of a field and industry and improve your thinking and skills.
  • COMPETITIONS: Competitions such as ‘Kaggle’ are held to bring out the best in the participants. They help in improving analytical and problem-solving skills. As these competitions offer a restrictive environment, it helps bring out innovative and creative ideas and solutions.

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:

  • Google Analytics is an analysis tool used by small companies to gather, store, analyze and study the data. This tool can be used when there is limited resources and limited data to work with.
  • Machine Learning Techniques are used on the data to extract and analyze the relevant data by mid-size companies. The data available to mid-size companies is substantial but not extremely specialized.
  • Various different Data Science methods like Visualization, Machine Learning, Artificial Intelligence are used by big companies to manage their data.

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:

  • BEGINNER LEVEL
    • IRIS DATA SET: The Iris Data Set is one of the most popular and widely accepted data sets. The set is resourceful, versatile and easy to use. It helps in identifying and recognizing patterns. The Iris Data Set is believed to be the easiest data set to incorporate during the learning period as it is easy and not complex or complicated with only 4 columns and 50 rows. It is one of the best data sets for any beginner in the field of Data Science.Practice Problem: Predict the class of a flower on the basis of these parameters.

    • LOAN PREDICTION DATA SET: Data Analytics and Data Science Methodologies are used extensively in the banking and finance sector. The Loan Prediction Data Set has been used to give learners a real-life experience of concepts which are used in this sector. The data set works by providing concepts like challenges faced, the strategies implemented and the variables that influence the outcomes. This data set is considered a problem data set and has 13 columns and 615 rows.
      Practice Problem: Predict if a given loan will be approved by the bank or not.

    • BIGMART SALES DATA SET: Retail sector is another industry that makes use of data analytics heavily. Optimization of business processes of retail companies is possible by using this data set. It helps the companies in basic operations such as Offer Customization, Product Bundling, Inventory Management etc. These tasks are handled effectively by using the tools and methodologies of Data Science and Business Analytics. This data set is also used in problems related to regression and has 8523 rows and 12 variables.
      Practice Problem: Predict the sales of a retail store.
  • INTERMEDIATE LEVEL
    • BLACK FRIDAY DATA SET: The Black Friday Data set refers to another set which caters to the retail sector. It captures the sales transactions from a retail store and analyzes the data to gain an understanding of the experiences of day to day shopping. The data set is set in order to explore and expand technical skills and capture the experiences of millions of customers. The set is considered a regression problem and has 550,069 rows and 12 columns.
      Practice Problem: Predict the amount of total purchase made.

    • HUMAN ACTIVITY RECOGNITION DATA SET: The Human Activity Recognition Data Set has 561 columns and 10,299 rows and has a collection of 30 human subjects. Smartphone recordings were used to collect the subject data. The smartphones used to record the data had inertial sensors which helped in data collection.
      Practice Problem: Predict the human activity category.

    • TEXT MINING DATA SET: The Text Mining Data Set was introduced in the year 2007 in the Siam Text Mining Competition. The data set has reports related to aviation safety which help in discovering problems encountered on certain flights. The Text Mining Data Set is a multi-classification and high dimensional problem with 30,438 and 21,519 columns.
      Practice Problem: Classify the documents on the basis of their labels.
  • ADVANCED LEVEL
    • URBAN SOUND CLASSIFICATION: A beginner in the field of Machine Learning can easily solve problems like Titanic survival prediction by using simple and very basic Machine Learning tools and methodologies. Unlike such problems, real problems are more complicated and complex which are harder to calculate, analyze and provide a solution for. The Urban Sound Classification data set helps in finding solutions to the real-world concept of Machine Learning. Along with this, it also helps in understanding, introducing and implementing the process of Machine Learning. The data set has 8,732 clippings which are categorized into 10 classes of urban sounds. The developer is introduced to real-world scenarios of classification and various concepts of audio processing.
      Practice Problem: Classify the type of sound that is obtained from a particular audio.

    • IDENTIFY THE DIGITS DATA SET: The Digits Data set has a collection of 7000 images, which have dimensions of 28x28 each. The total required for these images is 31 MB. By using this data set, a developer can easily study, analyze, recognize and classify the images according to the elements present in them.
      Practice Problem: Identify the digits present in a given image.

    • VOX CELEBRITY DATA SET: Audio processing is one of the most important as well as developing field in Deep Learning. The Vox Celebrity Data Set works by identifying a speaker at a large scale. The data set is a collection of voices, words and sentences taken from YouTube videos of celebrities. It plays an essential role in isolating and recognizing the voice. The data set has 100,000 words which have been spoken by 1,251 speakers and celebrities around the globe.
      Practice Problem:
      Identify the celebrity that a given voice belongs to. 

How to Become a Data Scientist in Cape Town, South Africa

These steps will guide you in the direction to becoming a top-notch Data Scientist:

  1. Getting Started: The first step in the world of Data Science must be programming language. Various languages are used by companies all over the world. Choose a programming language that interests you the most and matches with the kind of work you wish to do. A great starting point would be Python or R Language.
  2. Mathematics and Statistics: Data Science is incomplete without Mathematics and Statistics. The data may be numerical, textual or an image. You should have clear knowledge about handling data and making and identifying patterns and relationships between numerous sets of data.
  3. Data Visualization: This is one of the most essential skills required to become a Data Scientist. Without a creative approach, data visualization will not be possible. Understanding, Analyze and Simplifying the data for non-technical team members requires extensive data visualization. It becomes an important tool for communication between teams and departments.
  4. Machine Learning and Artificial Intelligence: Machine Learning and Artificial Intelligence have seen a rapid rise in the past decade. No Data Science project is complete without the two. In-depth knowledge of Machine Learning and Artificial Intelligence will help you be more efficient and productive.

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:

  1. Degree/Certificate: A basic course that goes over the fundamentals of Data Science is important and essential to start a career in Data Science. The course that you take up can be a physical, offline, classroom-based one or an online one. Through this course, you will learn the basics of the field and learn how to use the tools and methodologies of Data Science in real life. Along with the knowledge, the degree or the certification will also give you career a boost. As Data Science is an emerging field, there are continuous advancements which require professionals to keep up with the pace. Continuous learning is required to become a master in this field. Statistically, due to the advancing nature of the field, it has been seen that Data Scientists have the most PhDs when compared to other job titles. 
  2. Unstructured Data: One of the major tasks of a Data Scientist is to look for and identify patterns in a certain set of data. Most of the times, the data that reaches these professionals is unstructured and doesn’t fit into the premade databases. In such cases, the complexity of the whole process increases as the data has to be structured and organized before the analysis begins. The role of a Data Scientist is to understand the patterns and manipulate the unstructured data.
  3. Software and Frameworks: There is a large amount of unstructured data which makes it essential to use popular and powerful tools which sort and organize the data. These software and frameworks are mostly programming languages such as Python and R. professionals prefer to use R over Python to organize unstructured data.
    a. R is one of the most used programming languages despite the fact that the learning curve is quite steep. It is one of the best tools to solve statistical problems.

    b.  Hadoop is another language which is frequently used by data scientists to manage and store excess data. This language is used when the data available is more than the memory at hand. It is a quick and effective way to convey data to various points in the machine. Similarly, Apache Spark is also used to cover the same grounds as Hadoop. It is becoming one of the best tools for computational work as it is easier and faster than Hadoop. One of the best features of Apache Spark is that it helps in preventing any data loss that might occur when so much of data is being handled on a daily basis.

    c. Along with the programming languages, it is essential to learn about the field itself. Aspiring data scientists should also have a fair knowledge of databases and database management. SQL queries is another tool which is used extensively in Data Science. A data scientist is expected to have sufficient knowledge about the same.

  4. Machine Learning and Deep Learning: Once the data has been gathered, organized and prepared, application of algorithms to get the desired results is the next step. Specific algorithms offer deep, accurate and better analysis. Deep Learning is used to deal with the data that has been provided.

  5. Data Visualization: This is one of the most essential skills required to become a Data Scientist. Without a creative approach, data visualization will not be possible. Understanding, Analyze and Simplifying the data for non-technical team members requires extensive data visualization. It becomes an important tool for communication between teams and departments. Data Visualization helps in making informed decisions as the data is extensively analyzed. A Data Scientist studies the raw data and prepares reports in the form of graphs and charts, which are easy to understand and comprehend. Some tools used for this purpose include matplotlib, ggplot2 etc.

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 – 

  • Networking – Education is the perfect platform for networking. During the years one pursues a degree or a certification, there is a perfect opportunity to build contacts and have great mentorship. Like every other field, networking plays an important role in the success of any business.
  • Structured learning – A formal education ensures that schedules and deadlines are followed. This helps in grooming the students for real-life scenarios. Following tight schedules and keeping up with the pace of the company you would eventually work for is taught in college.
  • Internships – Internships are another very major aspect in flow of practical education. Internships give students hands-on experience by making them work on live projects.
  • Recognized academic qualifications for your résumé – A degree will not only help you secure a good job but will also add value to your CV. This will help you take up further studies as well as get better opportunities in the future.

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.

  • Bachelors in strong STEM (Science/Technology/Engineering/Management) – 0 points
  • Bachelors in weak STEM (Biochemistry/Biology/Economics) – 2 points
  • Bachelors in non-STEM – 5 points
  • Work Experience in working with Python (less than 1 year) – 3 points
  • Work Experience in Coding (less than 1 year/none) – 3 points
  • Weak Independent Learning – 4 points 
  • You do not understand that this scorecard is a regression algorithm: 1 point

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:

  • Data Sets: Data sets are an important part of Data Science. Data Scientists work with data sets on a daily basis. Due to the amount of data in these data sets, it is impossible to analyze the data without a proper programming language. It helps in storing, understanding and analyzing the data.
  • Statistics: Data Science involves data and statistics. A data scientist’s efficiency is increased when programming is used to work with statistics. Knowledge of statistics is not enough to work efficiently – use of programming will make the process faster and easier.
  • Framework: If a Data Scientist is a proficient programmer, it increases his/her ability to perform the tasks in an efficient manner. Programming also helps organizations in building systems to create frameworks. These framework work by automatically analyzing and visualizing the data available. The frameworks also help in pipelining the large amounts of data monitored by large organizations.

Data Scientist Jobs in Cape Town, South Africa

Given below is a list of steps that should be followed in a sequence to get a job in Data Science:

  1. Getting started
  2. Mathematics
  3. Libraries
  4. Data visualization
  5. Data preprocessing
  6. Machine Learning and Deep Learning
  7. Natural Language processing
  8. Polishing skills 

  1. Getting started: The first step towards starting a career as a Data Scientist is to learn a programming language. It is suggested to learn a language you feel comfortable with. The programming language should also be chosen according to the work that you would want to do as Data Scientists. Python or R Language is a good place to start with.
  2. Mathematics: It is important to understand, that Data Science uses concepts of Mathematics at the base of the operations. It is all about making sense of raw data by finding relationships and patterns between different sets of data. It is essential for a Data Scientist to be proficient in Mathematics. Along with this, Statistics also plays an important role in the field of Data Science. Statistics and Mathematics work together to manage the huge amounts of data on a daily basis. The main focus should be on Mathematical topics such as Probability, Inferential Statistics, Linear Algebra and Descriptive Statistics. 
  3. Libraries: There are various tasks involved in Data Science process which includes preprocessing of the raw, unstructured data and plotting the structured data. Along with this, Machine Learning algorithms are also applied to the processed data to study and analyze it. Some famous libraries which help in these tasks are:
    1. NumPy
    2. ggplot2
    3. SciPy
    4. Pandas
    5. Matplotlib
    6. Scikit-learn
  4. Data Visualization: It is the responsibility of a Data Scientist to make sense of any data. Data is processed by finding patterns and relationships which help in making the organization as simple as possible. Visualization of data is the best way to process the data. A popular way to do is by using Graphs. There are numerous libraries used for this purpose:
    1. Matplotlib - Python
    2. Ggplot2 - R
  5. Data preprocessing: It is important to preprocess the unstructured data before the data analysis can begin. Feature engineering and variable selection method is used to pre-process unstructured data. Once the data has been structured, Machine Learning tools are introduced to analyze the data.
  6. Machine Learning and Deep Learning: It is important to incorporate methods of both Machine Learning and Deep Learning while analyzing data. Deep Learning is highly preferred for analysis of data because it is capable of handling large amounts of data. Every Data Scientist should focus on neural networks, CNN and RNN. 
  7. Natural Language processing: National Language Processing is the processing and classification of text form of data. Data Scientists should be familiar with this concept to make their work easier, faster and more efficient.
  8. Polishing skills: Competitions and tournaments help in polishing skills. Data Scientists get great platforms to exhibit their skills in the form of competitions such as Kaggle. Skills can also be updated and polished by doing live projects.

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:

  • Study: A deep knowledge of the field and the methodologies of Data Science would give you an edge over other aspiring Data Scientists. Focus on topics like:
    • Statistics
      • Machine Learning
      • Probability
      • Statistical Models
      • Neural Networks
    • Meetups and Conferences: Tech meetups, Data Science Conferences, Machine Learning and Artificial Learning conferences are great ways to network as well as learn more about the field.
    • Competitions: Competitions and tournaments help in polishing skills. Data Scientists get great platforms to exhibit their skills in the form of competitions such as Kaggle.
    • Referral: Referrals are a great way to land interviews for your dream job. Networking and building professional connections are important for referrals. Professional online platforms such as LinkedIn etc are also a great way to build connections.
    • Interview: Be prepared for the interview by studying the field and looking over the company profile.

    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:

    • Categorizing and filtering data which is relevant for the advancement of the business.
    • Managing large amounts of data and finding patterns and relationships.
    • Managing structured as well as unstructured data. 
    • Organizing the relevant data extracted from the main data pool.
    • Creating techniques and methodologies to benefit from this data by using Machine Learning and Deep Learning techniques.
    • Statistically analyzing the data to predict the outcomes and growth of the company.

    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:

    • Type of Company-
      • Startup Companies – Highest Pay
      • Public Sector – Medium Pay 
      • Government and Education Sector – Lowest Pay
    • Roles and Responsibilities-

      • Data Scientist – R 596,400 per year
      • Data Analyst – R 206,716 per year
      • Database Administrator – R 308,872 per year

    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 – 

    • Talks by data scientists
    • From academia to Zoona and Eskom Customer Segmentation Learning
    • CT Data Science meetup: Machine learning
    • Big data in radio astronomy and more 

    Networking is the key to get hired in a top-notch company. Building contacts and networking can be done through the following channels – 

    • An online platform like LinkedIn
    • Data science conferences like Machine Learning Conferences, Data Mining Conferences, Artificial Intelligence Conferences
    • Competitions and Tournaments
    • Social gatherings like Meetup

    Top 8 Data Science Career Opportunities in 2019 in Cape Town are -

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

    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 -

    • Education: As mentioned before, data scientists have more PhDs compared to other professionals in other fields. A degree will be useful while searching for a job. Additional certifications and diplomas may also be beneficial.
    • Programming: Programming is something that Data Scientists use every day. Proficiency in the programming languages used in the field of Data Science will help you get better job opportunities.
    • Machine Learning: After the data has been prepared and organized, the next step is the analysis of the data. Machine Learning is used by Data Scientists to complete this task. Therefore, having Machine Learning skills is a must.
    • Projects: As Data Science is a practical field, it is important to get some hands-on experience before looking for jobs. Projects offers the best platform to practice and hone your skills.

    Data Science with Python Cape Town, South Africa

    • Python is a programming language which is multi-paradigm. This means that the various facets of the language are suitable for the different types of work involved in Data Science. The language is structured and is object-oriented. It has numerous libraries and packages which are beneficial for the purpose of Data Science.
    •  Python works on a very simple interface and has high readability. This is one of the main reasons why it is preferred by Data Scientists. There a large number of libraries which are dedicated to analytical research. Along with this, the language also offers packages which are specifically made for Data Science.
    • The language has a wide range of resources which makes it a choice language among the Data Scientists. There are tools which make the work of Data Scientists easier. These resources are available for use whenever someone encounters any problem in developing a Python program.
    • The Python community is vast which gives the language an edge over the other programming languages. As there are millions of developers using this language, resolving problems becomes faster and easier. Most of the times the resolution to a problem has already been found as there are other people who have been stuck in the same position previously. This allows the Data Scientists to be flexible. The Python community is large and helpful.

    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:

    • Download: Visit the official Python website (www.python.org) and download the software from there. (www.python.org/downloads/)
    • Setup: Begin the setup procedure and click on the checkbox at the bottom of the box. This will add Python 3.x to PATH and allow you to use the functionalities of Python from the terminal.

    • Alternate Method: Python can also be installed on Windows through Anaconda. Run the following command to check whether the version is installed or not – python –version
    • Update and Install: After checking, give the ‘Install’ command and update the libraries by following this command –
      Python -m pip install -U pip

    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:

    • Install Xcode: Apple’s Xcode package is needed to install brew. Follow this command - $ xcode-select –install
    • Install Brew: The next step is to install Homebrew which is a package manager for Apple by following this command -
      /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

    Type ‘brew doctor’ to confirm installation,

    • Install Python 3: Use brew to install Python. Follow this command to check the version- python –version

    Alternatively, virtualenv can also be used to install Python. This will help in creating isolated places where different projects can be run separately.

    Data Science with Python Certification Course in Cape Town

    The most enduring image of Cape Town is a city under the shadow of the soaring Table Mountain, shimmering beaches with golden sands and modern and ancient architecture standing side by side in the heart of the city. Its multicultural and multi-ethnic population adds to its charms and the city embraces you with open arms. A hub of technology, commerce and trade, Cape Town significantly contributes to the GDP of South Africa. Its real estate, insurance, banking, technology, manufacturing, shipping, and retail sectors offer plenty of job opportunities. It is home to such national and international giants as Woolworths, Naspers, Capitec Bank, Johnson & Johnson, GlaxoSmithKline, Levi Strauss & Co, Adidas and several others. Tourism is also a major contributor and visitors come to see the famous harbour and other iconic monuments such as Bo-Kaap, Simon?s Town, and the Dutch style buildings that dot the city. For a chance to work in this vibrant city you can pursue one KnowledgeHut?s several courses such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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