The programs are designed to create a solid foundation for first-time job seekers. The courses are designed to be groundbreaking covering many fields, each of which gives students a solid foundation, helping students to constantly learn, not only in work but also in life.
The Career Series program for graduates and non-graduates always ensures to meet the diverse work needs of each student. The goal of this program is to help students familiarize themselves with the knowledge and skills needed to become competent data analytics professionals and be sought after by employers.
When looking at the names of the courses, you can clearly visualize the results, the purpose of achieving them, but in fact, the training content of each course has a quality that is really superior to expectations.

  • Project-based learning helps students apply their knowledge to real-life situation simulation projects.
  • Continuous goal guidance sessions help students and lecturers focus on the nearest goal, thereby continuously evolving throughout the clearly designed learning process.
  • There are 3 interlocking provisions in each career training program series, which help clarify the vision and goals to be achieved, which is a clear difference from other mid-ranges.
  • Each student is instructed to practice to assess their level, which brings comfort to students. In a comfortable, guided learning environment that allows students to practice things that haven’t been taught in class and can practice more depending on their abilities, which is the difference employers are looking for.


Statistics for Analytics

  • Introduction to Statistics and Analytics
  • Data Representation
  • Forecasting Techniques
  • Causality and Correlation
  • Hypothesis Testing
  • Parametric and Non-Parametric Tests

Programming in R

  • Overview of R and R Studio
  • Introduction to R libraries
  • Data structures and data types in R
  • Variables and Operators
  • Functions in R
  • Statements in R

Machine Learning

  • Introducing Machine Learning
  • Managing and Understanding Data
  • Lazy Learning – Classification Using Nearest Neighbors
  • Probabilistic Learning – Classification Using Naive Bayes
  • Divide and Conquer – Classification Using Decision Trees and Rules
  • Regression Methods


Data Cleaning (R)

  • Reading data
  • Cleaning and Preprocessing data
  • Data Imputation
  • Creating tidy data

Data visualization – R

  • Charts and exploratory graphs
  • Plotting functions

Advanced R programming

  • Family of function
  • Regular Expressions and Data Structure Manipulation

Supervised learning

  • Classification techniques – decision tree and random forest
  • Regression – Simple, multiple and logistic regression
  • KNN (K Nearest Neighbors)

Supervised Machine Learning Algorithms

  • Classification techniques –SVM (Support Vector Machine), Naïve Bayes
  • Time Series Analysis

Unsupervised learning

  • Clustering techniques – K means clustering
  • Association Rule Learning
  • Dimensionality Reduction – Principal Component Analysis, Linear Discriminant Analysis

Ensemble Modelling

  • Bagging
  • Boosting
  • Adaptive Boosting (ADA BOOST)
  • Gradient Tree Boosting
  • XGBoost
  • Developing Models


Basis of Python programming

  • Basics of Python
  • Variables and Operators

Data types

  • Lists, Dictionary and functions

Data Exploration in Python

  • Working with Data in Python
  • Data Modelling using Machine Learning Techniques
  • Data Visualization

Visualization using Power BI

  • Power BI Desktop
  • Working with Data
  • Data Analysis Expression
  • Data Visualization

Advanced Visualization in Power BI

  • Custom Visualization
  • Exploring live connections
  • Power BI Development API & Integrations

Artificial Intelligence

  • Introduction to Neural network
  • Understanding Tensor flow
  • Convolutional Neural Networks
  • Recursive Neural Networks
  • Building a model

Marketing and Operations Analytics

  • Marketing: Brand Analytics, Basket optimization, Effective cross-selling, social Media Analytics
  • Operations: predicting project effort and productivity, capacity planning and management, building early warning system
  • Making Effective Presentations
  • Presentation Skills
  • Live Presentation to assess the learnings
  • Professional Skills & Interview Preparation
  • Recruitment _Interview Process_ CV preparation
  • Preparing for the Interviews
  • Mock Interview

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Gain expertise in front-end, back-end, web development, application development, and software development technologies at different levels.


The course covers introductory elements of object-oriented programming and advanced concepts like error handling, file handling and Python libraries.



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