About the Program
About The Certified Data Scientist Program
The data scientist role is an offshoot of the statistician role that includes the use of advanced analytics technologies, including machine learning and predictive modeling, to provide insights beyond statistical analysis. The demand for data science skills has grown significantly in recent years as companies look to glean useful information from the voluminous amounts of structured, unstructured and semi-structured data that a large enterprise produces and collects — collectively referred to as big data.
A data scientist uses large amounts of data to develop hypotheses, make inferences and hone in on customer, business and market trends. The data scientist must be able to communicate how to use analytics data to drive business decisions that may include changing course, improving a process or product, or creating new services or products.
Course Objective
This is a complete course that provides you detailed understanding of data science, encompasses basic statistical concepts to advanced analytics and predictive modeling techniques, along with project life cycle, data acquisition, analysis, statistical methods and machine learning.
The objective of the course is to learning statistical analysis techniques and tools to solve business problems that help you to emerge as ‘Industry Ready’ professional in the field of Data Science.
You will be learning the Data Science skills with the most popular and leading analytical tools widely used across industries such as R.
Upon completion of Data Science course, you now have acquired valuable skills to
- Master key facets of data investigation, including data wrangling, cleaning, sampling, management, exploratory analysis, regression and classification, prediction, and data communication.
- Implement foundational concepts of data computation, such as data structure, algorithms, parallel computing, simulation, and analysis.
- Leverage your knowledge of key subject areas, such as statistical quality control, exponential smoothing, seasonally adjusted trend analysis, or data visualization.
Who should do this course?
- Graduates / Professionals from various quantitative backgrounds like Engineering, Finance, Mathematics, Statistics, Business Management who aspires to spearhead their career in Data Analytics.
- Basic knowledge of data analysis & business problems
- Analytics consultants
- IT/Software Professionals
Who are the trainers?
Our trainers are highly qualified industry experts and certified instructors with more than 10 years of global analytical experience.
Prerequisites
Prior knowledge in basic programming / statistics is recommended for this course.
What do past students say about the Certified Data Scientist course?
Learn from practitioners, not from trainers.
“R classes trained by Databyte Academy, has been more than meeting my expectations. The trainer gave a very detailed explanation and examples of case studies, both in the fundamental of statistics and also the concept around Business Analytics. I’ve gained an in-depth knowledge on how to translate a real-world business problem into statistics problem.”
Ivan Gan Manager, Naviworks S/B
I have gained valuable experience and knowledge from the R-Programming classes. I liked the delivery of the training. Mr. Sunit was very helpful and made the course easier to follow even for a beginner. He can answer every question asked and even provided us with examples that we can easily relate to. Looking forward to other courses.
Nurul Amalina Syaza Sabri Data Analyst ValueCAP Sdn. Bhd
Firstly, I’m satisfied with the content structure and the pace of the R for Data Science class. Mr. Sunit Prasad managed to present the course contents in an organised and interesting manner. He made sure to assess and build the foundation of the students in R programming before proceeding to teach us about the commonly used statistical analysis in the data science industry. Overall, this 5-day R for Data Science course met my expectations and I’m looking forward to completing the remainder of the Certified Data Scientist course.
Mah Chee Weng Student
Exam & Certification
The certification is provided by Databyte Academy
Upon successful completion of the program, students will be conferred with dual certification:
- Certificate of Completion
- CERTIFIED DATA SCIENTIST*
In order to be “Certified” as part of the course, students need to complete the assignments and examination. Once all your assignments are submitted and evaluated, the certificate shall be awarded.
New Intake :
Full Time – Will be commencing soon
Certified Data Scientist
Course ID – CDS
Duration – 40 Hours / 5Days
Tools – R.
Learning Mode – Instructor Led-Classroom Training
Next Batch – Full Time
Course Outcome
Ability to use advanced analytics techniques and tools to improve business performance across many functions by managing data with help of different tools like R and Python. Working with various forms of structured and unstructured real time data sets in solving business problems with advanced statistical techniques & algorithms like machine learning
Course Content
The field of data analysis, as the name implies, analyses data to discover trends. It has tremendous uses not only in the economics and financial sector but fields like law, healthcare, public administration, politics, telecom, social media, manufacturing, banking & financial institutions etc. who rely on quality data analysis to arrive at strategic business decisions. Working professionals can definitely improve their resume and their job prospects by achieving a certificate in data analytics.
Introduction to Data Science
- What is Data Science?
- Analytics vs. Data warehousing, OLAP, MIS Reporting
- Relevance in industry and need of the hour
- Types of problems and business objectives in various industries (Regression, classification, segmentation, forecasting, optimization etc)
- How leading companies are harnessing the power of analytics?
- Critical success drivers
- Different phases of a typical Analytics projects
- Understanding Heuristic vs. statistical models/analysis
- Understanding classical techniques vs. machine learning techniques
- Latest Trends in data science
- Opportunities with data science
R: Introduction to R- environment
- The Workspace
- Input/ Output
- Useful Packages (Base & other packages) in R
- Graphic User Interfaces (R studio)
- Customizing Startup
- Batch Processing and reusing Results
R: Data Input & Output (Importing & Exporting)
- Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists)
- Importing Data from various sources
- Database Input (Connecting to database)
- Exporting Data to various formats)
- Viewing Data (Viewing partial data and full data)
- Variable & Value Labels – Date Values
R: Data Manipulation
- Introduction to MS Access
- What is SQL – A Quick Introduction
- Getting started
- Understanding basic RDBMS concepts
- Data manipulation – Reading & Manipulating a Single Table
- Data based objects creation (DDL Commands)
- Optimizing your work
- Data manipulation – CaseStudy-1
- Data manipulation – Case Study-2
Data Analysis – Visualization
- Creating Graphs
- Histograms & Density Plot
- Dot Plots – Bar Plots – Line Charts – Pie Charts – Boxplots – Scatterplots
R: Basic Statistics (Exploratory Analysis)
- Univariate Analysis
- Bi-Variate Analysis (correlation, association etc)
- Descriptive Statistics(central tendency/variance)
- Frequency Tables /Summarization
- Exploratory Analysis
- Probability distributions
- Sampling – Central Limit Theorem
- Inferential statistics – Hypothesis testing
- Statistical tests (t/z-test, ANOVA, chi-square etc)
R: Data Prep & Reduction techniques
- Data Audit report creation and understanding
- Need for data preparation
- Binning, Dummy and Derived variable creation
- Standardization, Normalization
- Outlier treatment
- Missing values treatment (MI, clustering, regression based)
- Dimension reduction – Factor Analysis – PCA
R: Customer Segmentation
- Basics of clustering
- Heuristic segmentation (RFM, Life stage, value based etc..)
- Cluster analysis (K-means and Hierarchical)
- Objective & subjective segmentation
- Decision Trees (CHAID/CART/C5.0)
- Cluster evaluation and profiling
- Interpretation and application
R: Regression Modelling
- Basics of regression analysis
- Approach: Model Estimation, OLS, MLE & Error Function for finding parameters, Assumptions verification (Linearity, Normality, multicollinearity, outliers etc)
- Linear regression Model fitting
- Logistic regression Model Fitting
- Measures of good ness of fit (R^2, Adj R^2, Concordance, Gini, KS, Lift etc…)
- Model Diagnostics – Residual Analysis– Decile Analysis – ROC Curves etc..
- Interpretation of results
R: Time Series Forecasting
- Time Series Introduction / Regression on Time, Time Series components
- Modelling Seasonality as Deviation
- Basic methods(pattern & pattern less)
- Averages (MA, WMA, CMA etc)
- Standardization, Normalization
- Outlier treatment
- Missing values treatment (MI, clustering, regression based)
- Dimension reduction – Factor Analysis – PCA
R: Introduction to Machine Learning
- Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning,
- Different Phases of Predictive Modelling
- Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
- Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)
- Cost & optimization functions
R: Machine Learning in Practice
- Ensemble Learning (Random Forest, Bagging & boosting)
- Artificial Neural Networks(ANN)
- Support Vector Machines(SVM)
- KNN, Naïve Bayes
- Text Mining & NLP
Get Ahead with Databyte’s Certificate
Earn your Certificate
Our Certified Data Scientist Program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.
Differentiate yourself with a Certificated Data Scientist Program
The knowledge and skills you’ve gained working on projects, simulations, case studies will set you ahead of competition.
Share your achievement
Talk about it on Linkedin, Twitter, Facebook, boost your resume or frame it – tell your friends and colleagues about it.
Learning Path
