Data Analytics using Python and R Programming
Fast-track your career with "Data Analytics using Python and R programming". Use Python and R to perform Exploratory Data Analysis and Data Mining on Big Data. Master Machine Learning, Neural Networks, Descriptive/Predictive modelling with Python and R for better business decision making.
On-campus training in Malaysia: 48 hours
Data Analytics Certification Programme Overview
This certification program provides an overview of how Python and R programming can be employed in Data Mining of structured (RDBMS) and unstructured (Big Data) data. Comprehend the concepts of Data Preparation, Data Cleansing and Exploratory Data Analysis. Perform Text Mining to enable Customer Sentiment Analysis. Learn Machine learning and developing Machine Learning Algorithms for predictive modelling using Regression Analysis. Assimilate various black-box techniques like Neural Networks, SVM and present your findings with attractive data visualization techniques.
Data Analytics Training Learning Outcomes
Data Analytics Course Modules
Data Science is the sexiest job of 21 century. It is applied in all the sectors and domains across the globe. The study deals with descriptive, predictive analytics which enables the management to run the business with profits and stay ahead of the competition. Learn how Data Science is applied in various sectors by exploring the use cases.
A Data Science project also needs project management like any other project. CRISP-DM process is used to handle the Analytics project. You will learn the various stages of project management for Data Analytics projects. Understand the finer concepts of each stage.
Predictive Analytics will help in estimating a value upfront to simulate the business conditions and management can leverage this information to devise better strategies. To come up with Predictive analytics algorithms the data needs to be arranged into the desired form with all the relevant details in a table representation. As part of this module, you will learn how to perform Data Cleansing, preparation of data and learn about business moments to perform Descriptive analytics.
As part of the data preparation steps, you might see a few details may not be available. Learn how to construct new features using the existing features. Learn to extract data from various sources. Understand the difference between the forward and backward feature selection techniques.
Assumptions for any condition/problem of a business needs to be evaluated. Learn how Hypotheses are analyzed using Inferential statistics concepts. Learn Type I and Type II errors. Learn how to deal with continuous and discrete value evaluation using various Hypothesis testing use cases. Learn about Data Mining and the underlying techniques to process the data. Use Python and R Programming for application of a wide variety of Machine Learning algorithms. You will try to remember the equation of a straight line and learn how that equation is used for the prediction of values to solve a business problem. Evaluation techniques are explained by calculating the measure of Error (RMSE). Learn to deal with various problems while building Regression Models like Collinearity, Heteroscedasticity, Overfitting, and Underfitting.
Learn about LINE assumptions while constructing Regression models. Learn how to handle binary data prediction using generalized linear models. Learn about logit function and probability function used for the prediction of a binary value. Understand the components of the Confusion matrix which is the evaluation technique used for models used to predict categorical data. You will also learn about discrete probability distributions and Regression models used to predict count data.
Data Mining unsupervised learning is used as an EDA to derive insights. As part of this module, you will learn concepts like Clustering to identify homogeneous data records from heterogeneous data records. Learn how pattern mining can be performed using Market Basket Analysis. Association rules are used to measure the relationship between entities and thereby devise new offers to make profits.
The majority of the data that is generated today is in an unstructured form, out of which the majority of the data is in Text format. As part of this course learn about Text analytics. The unstructured data needs to be transformed into a structured form for analysis. Learn about the Bag of Words concept. Learn to extract data from e-commerce sites and travel sites. Natural language processing is in high demand. Learn about NLP concepts of Emotion mining, topic modelling as part of this tutorial. Using R and Python, learn how to work with libraries that are used for data extraction, and sentiment analysis.
Supervised learning classifiers are used for predictive analytics. The lazy learner which is based on distance measures is used for predictions. Learn how to identify the best k value in the kNN classifier. Learn about the rules-based technique Decision Tree and their nodes and features. You will learn about the ensemble technique Random Forest to reduce the overfitting and improve the accuracy of the Decision Tree model.
The most commonly used algorithms in Machine Learning are Neural networks and SVM. The mathematical explanation for these algorithms is unknown hence these are also called Black box techniques. Learn about the concepts of Neurons, Perceptron algorithm and understand how Neural networks try to replicate a biological brain. Support Vector Machines is another powerful Deep learning algorithm, used for classifying the data in a multidimensional space using kernels.
Time series analysis is performed on data that is collected concerning time. The difference between a Time series data and Cross-sectional data is explained as part of this module. Forecasting techniques explain the response variables variations based on time. Get introduced to the time series components, and the various visualization techniques to interpret the components. Understand the different types of Forecasting techniques available to churn the data.
Python was ranked 3rd and R 5th in the top 16 most popular programming languages in Malaysia.
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Who Should Sign Up?
- IT Engineers
- Data and Analytics Manager
- Business Analysts
- Data Engineers
- Banking and Finance Analysts
- Marketing Managers
- Supply Chain Professionals
- HR Managers
- Math, Science and Commerce Graduates
Data Analytics Training in Malaysia has gathered steam and will evolve in the years to come. The advent of Big Data has led to a data explosion of sorts. Big Data Analytics has opened myriad opportunities for students and working professionals. The Data Analytics Certification Program includes an introduction to foundation Data analytics as well as Advanced Data Analytics using Python and R programming. Students who complete this course can command very high salaries in Malaysia and other countries. Python which is the top programming tool and R which is second best allows you to accomplish a lot of routine data analytics tasks with ease.
Malaysia with its peninsular mainland in the South - Eastern part of Asia is rich in Flora & Fauna. It is one of the fastest-growing economies and has embraced emerging technologies from around the globe. Many industries in the federal capital region of Kuala Lumpur are now using advanced Data Analytics for their business operations. Petronas, the leading company in Kuala Lumpur has been using Data Analytics for quite some time. Analytics professionals with knowledge for Python and R are now very much in demand because businesses are obsessed with Data Analytics is driven decision making.
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Data Analytics Panel of Coaches
Bharani Kumar Depuru
- Areas of expertise: Data Analytics, Digital Transformation, Industrial Revolution 4.0.
- Over 14+ years of professional experience.
- Trained over 2,500 professionals from eight countries.
- Corporate clients include Hewlett Packard Enterprise, Computer Science Corporation, Akamai, IBS Software, Litmus7, Personiv, Ebreeze, Alshaya, Synchrony Financials, Deloitte.
- Professional certifications - PMP, PMI-ACP, PMI-RMP from Project Management Institute, Lean Six Sigma Master Black Belt, Tableau Certified Associate, Certified Scrum Practitioner, AgilePM (DSDM Atern).
- Alumnus of Indian Institute of Technology, Hyderabad and Indian School of Business.
Sharat Chandra Kumar
- Areas of expertise: Data Science, Machine Learning, Business Intelligence and Data Visualisation.
- Trained over 1,500 professional across 12 countries.
- Worked as a Data Scientist for 14+ years across several industry domains.
- Professional certifications: Lean Six Sigma Green and Black Belt, Information Technology, Infrastructure Library.
- Experienced in Big Data Hadoop, Spark, NoSQL, NewSQL, MongoDB, R, RStudio, Python, Tableau, Cognos.
- Corporate clients include DuPont, All-Scripts, Girnarsoft (College-dekho, Car-dekho) and many more.
- Areas of expertise: Data Science, Machine Learning, Business Intelligence and Data Visualisation.
- Over 20+ years of industry experience in Data Science and Business Intelligence.
- Trained professionals from Fortune 500 companies and students from prestigious colleges.
- Experienced in Cognos, Tableau, Big Data, NoSQL, NewSQL.
- Corporate clients include Time Inc., Hewlett Packard Enterprise, Dell, Metric Fox (Champions Group), TCS and many more.
Earn a certificate and demonstrate your commitment to the profession. Use it to distinguish yourself in the job market, get recognised at the workplace and boost your confidence. The Data Analytics Certificate is your passport to an accelerated career path.
FAQs for Certification in Data Analytics
While there are a number of roles pertaining to Data Professionals, most of the responsibilities overlap. However, the following are some basic job descriptions for each of these roles.
As a Data Analyst, you will be dealing with Data Cleansing, Exploratory Data Analysis and Data Visualisation, among other functions. The functions pertain more to the use and analysis of historical data for understanding the current state.
As a Data Scientist, you will be building algorithms to solve business problems using statistical tools such as Python, R, SAS, STATA, Matlab, Minitab, KNIME, Weka etc. A Data Scientist also performs predictive modelling to facilitate proactive decision-making. Machine learning algorithms are used to build predictive models using Regression Analysis.A Data Scientist has to develop expertise in Neural Networks and Feature Engineering.
A Data Engineer primarily does programming using Spark, Python, R etc. It often complements the role of a Data Scientist.
A Data Architect has a much broader role that involves establishing the hardware and software infrastructure needed for an organisation to perform Data Analysis. They help in selecting the right Database, Servers, Network Architecture, GPUs, Cores, Memory, Hard disk etc.
Different organisations use different terms for Data Professionals. You will sometimes find these terms being used interchangeably. Though there are no hard rules that distinguish one from another, you should get the role descriptions clarified before you join an organisation.
With growing demand, there is a scarcity of Data Science professionals in the market. If you can demonstrate strong knowledge of Data Science concepts and algorithms, then there is a good chance that you can carve a successful career in this domain.
360DigiTMG provides internship opportunities through Innodatatics, our USA-based consulting partner, for deserving participants to help them gain real-life experience. This greatly helps students to bridge the gap between theory and practice.
There are plenty of jobs available for Data Professionals. Once you complete the training, assignments and the live projects you can enroll for placement assistance. We help our students in resume preparation. Once the resume is ready we will float it organisations with whom we have formal agreements on job placements.
We also conduct webinars to help you with your resume and job interviews. We cover all aspects of post-training activities that are required to get a successful placement. After placement, we provide technical assistance for the first project on the job.
After you have completed the classroom sessions, you will receive assignments through the online Learning Management System that you can access at your convenience. You will need to complete the assignments in order to obtain your Data Scientist certificate.
In this blended programme, you will be attending 48 hours of classroom sessions over six days on campus in Kuala Lumpur, Malaysia. After completion, you will have access to the online Learning Management System for another three months for recorded videos and assignments. The total duration of assignments to be completed online is 40-60 hours. Besides this, you will be working on a live project for a month.
If you miss a class, we will arrange for a recording of the session. You can then access it through the online Learning Management System.
We assign mentors to each student in this programme. Additionally, during the mentorship session, if the mentor feels that you require additional assistance, you may be referred to another mentor or trainer.
No. The cost of the certificate is included in the programme package.
Heng Nguan Ting8 months ago
A company that give course from beginning level to advanced level. They will always keep in touch with their participant in order to get know about them and solve their problem accordingly. Nice place to start your learning.
Puteri ameena9 months ago
I joined the Data Science using R workshop and I really appreciated all the efforts that have been put into sharing the knowledge of Data Science. I learnt the reality of handling data unlike the theoretical classes we normally learn in university. I had so much fun too!! Thank you
Rong An Kiew9 months ago
I took part in the Jumpstart program 2018, I gained a lot of knowledge about Big Data from this program and there are also some experienced tutors teaching in this program. It provides some assignments to let us practise. Overall it is a good platform for learning Big Data.