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Data Science &
ML Engineering

Build expertise in data manipulation, visualization, predictive analytics, machine learning, and data science. With the skills you learn in a Nanodegree program, you can launch or advance a successful data career.

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There is one session available

Start September  30

This Program is part of Applied Data Science

About this Program

Build expertise in data manipulation, visualization, predictive analytics, machine learning, and data science. With the skills you learn in a Nanodegree program, you can launch or advance a successful data career. Start acquiring valuable skills right away, create a project portfolio to demonstrate your abilities, and get support from mentors, peers, and experts in the field. We offer five unique programs to support your career goals in the data science field.

1

FUNDAMENTALS OF ANALYTICS
 

2

MASTERING IN PYTHON
 

3

STANDARD SQL

 

4

BUILDING MODERN DATA WAREHOUSE BUILDING IN BQ

5

REAL-TIME DASHBOARD AND BI FUNCTIONS

6

EXTRACTION TRANSFORM AND LOAD TO BUILD MODERN DATA PIPE
 

7

BUILDING ANALYTICS PLATFORM IN GCP
 

8

CLOUD BASICS
AWS & AZURE

 

9

APPLIED DATA SCIENCE
 

10

APPLIED MACHINE LEARNING
 

11

APPLIED AI

 

12

CAPSTONE PROJECT

 

Who can take this course?

Applied Data Science & ML Engineering course is perfect for professionals who work with data and want to learn more about data manipulation, visualization, predictive analytics, machine learning, and data science. This course covers core concepts in the field and is a stepping-stone onto more advanced Applied Data Science & ML Engineering topics.

 

Common career paths for students who take the program are Business Intelligence, Asset Management, Data Analyst, Quantitative Analyst, Data Engineer, Data Science, ML Engineer and other finance careers.

Length

20 Weeks

Effort

8–10 hours per week

Price

BDT 80,000

Institution

Value Base

Eligibility

Anyone who has university level education

Level:

Operational

Language

English (Primary), Bengali, Norwegian

Instructors From World Class Industries and Academic institutions

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JUWEL RANA, PHD

Global Analytics Leader, Norway

Helping industry to simplify analytical needs by developing robust highly scalable analytical products

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Dr. SRH Noori

Associate Head at DIU, Bangladesh

Journey from Industry to Academy. Acting as the bridge between the industry and academia.

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Seraj Al Mahmud Mostafa

Data Scientist at Montana State University, USA

Passionate to optimising performance from Data Journey from Industry to Academy. Acting as the bridge between the industry and academia.

Content Blocks of VBA's BI & Analytics Engineer Program

Fundamentals of analytics

  • Understanding analytics processes
  • Knowing about important roles and responsibilities as
    • data scientists
    • data engineers
    • business analysts
    • market analysts
    • research scientists
    • analytics engineers and so on
  • Developing foundation around
    • Analytics platform
    • business intelligence platform
    • AI platform
    • Data platform
  • Understanding End to  end perspective of Data Product
  • Cross industry  alignment of analytical use cases such as
    • Telco,
    • Retail
    • Information Technology
    • Finance and Banking
  • Modern data science professionals way of working
    • Cross functional team​
    • Agile principles
  • Importance of capacity building in  Taking End to end responsibilities of developing data products
  • Data professionals proposition in both the global and local market
Investment Chart

Mastering in python

  • Know how to use Python Environment (week 1)
  • Know how to use Anaconda
  • Google colab for Python programming (week 1)
  • Introduction to Python 3 basics (week 1)
  • Understand Python script structure Know variables, naming, types, and operators (week 1)
  • Know strings, formatting, print (week 1)
  • Understand list, tuple, set, frozen set, dictionary (week 2)
  • Understand List comprehension (week 2)
  • Understand Dictionary comprehension (week 3)
  • Understand Control Structures: IF -Else, Loops (week 3)
  • Introduction to PANDA (week 4)
  • Importing dataset, creating data frame (week 4)
  • Know how to use Pandas in Data manipulation and analysis Understand Pandas Series and Data frames (week 4)
  • Know all on how to use NumPy and NumPy Arrays (week 5)
  • Understand Lambda function (week 5)
  • Understand the concept of Object in Python (week 5)
  • Know file manipulation/scripting (week 6)
  • Know error handling (week 6)
  • Know how to use Databases with Python (week 6)
Coding Station
 
 

STANDARD SQL &
BUILDING MODERN DATAWAREHOUSE INBQ

  • What is SQL?
  • Data Model - Understanding Data
  • Data Model - History of Data Model
  • Hierarchical, Network, Relational, Entity, Relational Semantic, NoSql
  • Data Model  - Relational Vs Transactional Model
  • SQL Statement Type
  • DML - Data Manipulation Language
  • DDL - Data Definition Language
  • DCL - Data Control Language
  • TCL - Transaction Control Language
  • Create Table, Physical Table, Temporary Table, Intermediary Table 
  • Table Vs Views
  • Adding Comments & Best Practices on coding
  • Retrieving Data with Select
  • Data Types
  • Filtering data with Where
  • Distinct and Case
  • Aggregate Functions
  • Count, SUM, Min, Max, Null & Data Type Handling
  • Filtering aggregate data with Having
  • Join Basics - 6 types of join, Inner Join, Right Join, Left Join, Full Join
  • Sub/Nested Queries
  • Window Ranking & Aggregation
  • Pivot & UnPivot
  • Large Complex Queries
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Real time dashboard and end to end business intelligence functions

  • Know the basics of DataStudio
  • Know how to connect Data Source
  • Ex. Google Marketing Platform products, including Google Ads, Analytics, Display & Video 360, Search Ads 360
  • Google consumer products, such as Sheets, YouTube, and Search Console
  • Databases, including BigQuery, MySQL, and PostgreSQL
  • Flat files via CSV file upload and Google Cloud Storage
  • Social media platforms such as Facebook, Reddit, and Twitter
  • In total Know how to import data from different sources to Data Studio
  • Know how to transform in Datastudio
  • Know how to create Reports in DataStudio
  • Tell your data story with charts, including line, bar, and pie charts, geo maps, area and bubble graphs, paginated data tables, pivot tables, and more.
  • Make your reports interactive with viewer filters and date range controls. The data control turns any report into a flexible template report that anyone can use to see their own data.
  • Include links and clickable images to create product catalogs, video libraries, and other hyperlinked content.
  • Annotate and brand your reports with text and images.
  • Apply styles and color themes that make your data stories works of data visualization art.
  • In total Understand how to use and modify Visualizations in Reports
  • Know how to create Dashboard in DataStudio 
  • Understand Data Studio Security
  • Know how to create Analysis Services Database with Datastudio
  • Know how to use Analysis Services with Datastudio
Woman Checking Data on Tablet
 

Building modern data pipe to automate extraction, transform and load processes

  • Learn details about data ingress and egress process
  • Build  and manage end to end ETL processes
  • Understand the meaning of entities, attributes and relationships
  • Know how ETL works in business settings
  • Know how ETL and BQ schedulers function
  • Learn Key principles of Building Modern Data pipes in Airflow workflow manager¨
  • Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.
  • Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
  • Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the powerful Jinja templating engine.
  • Scalable: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
  • Modern way of data collection for analytics. Gathering data into a data platform.
  • Batch processing Vs Real-time processing. How to process data with scalable frameworks, such Spark.
  • Connecting batch processing flows into robust pipelines.
  • Real-time processing. Data processing with scalable stream processing frameworks.
  • Deployment. Deploying batch processing applications in production.
A team discussion at a trading room
 

BUILDING ANALYTICS PLATFORM IN GCP

  • Functional layers of building modern AI platform
  • Data Lake​
  • Data Platform
  • End-to-End Data Product Deployment
  • End-to-End Business Intelligence Pipeline
  • Essential components of a data platforms
  • On the top services and applications to facilitate a data platform
  • Developing analytical products on GCP
Data on a Touch Pad
 

CLOUD BASICS (GCP, AZURE, AWS)

  • Learn basics of AWS services for Data Science
  • AWS S3 Storage
  • RDS
  • Redshift and Glue
  • Amazon Quicksight
  • Know basic GCP services for Data Science 
  • Know Google storage 
  • Understand
  • VM instances,
  • Google AI,
  • Google Datastudio
  • Know basics Azure services for Data Analysis 
  • Know Azure data storage 
  • Understand
  • PowerBI
Typing on a Computer
 

Applied data science
applied machine learning
applied ai

  • Overview of Data Science in terms of Exploratory Data Analysis
  • Overview of Machine Learning Techniques in Data Science
  • Building foundations in AI
  • ​Applied ML-Supervised
  • e.g. Support Vector Machine,
  • Linear Regression,
  • Logistic Regression.
  • Decision Tree,
  • K-nearest neighbour algorithms 
  • ​​Applied ML -Unsupervised
  • e.g. k-means
  • DBSCAN
  • Applied ML - Reinforcement:
  • Q-learning
  • ​Distributed ML Framework - Overview of Data Science Libraries in PySpark 
  • ​​Understand basic SciKit ML program structure: preprocessing, Data Splitting, Modeling, fitting, Training model, evaluation of model, predicting, cross-validation, storing and reusing a stored model with the new dataset  ​
  • Model Evaluation (Classification Accuracy, Confusion Matrix, Area Under Cover (AUC), Logarithmic Loss, F-Measure, RMSE, Mean Absolute Error) 
  • Deep learning + AutoML
A woman looking at charts on the screen
 

Capstone project building end-to-end data product industry level prod development

  • End-to-end data science project development
    Industry focused project implementation using relevant domain specific dataset
  • Implementation:
     
    Value Base Academy will offer capstone projects on four demanding areas as stated in the following from A to D. Students are free to select their own domain for the capstone project upon consultation with relevant instructors.
     
     
    ​​Project A: Telecom
     
    Project B: Sales & Marketing
     
    Project C: Information Technology
     
    Project D: Fintech, Finance and Marketing
     
    Project E: Retail
     
    Project F: Health Care
     
    Project H: Media and Digital Marketing Agencies
    Project I: Garments and Textile Industries
Financial Report