Become a Data Analytics and Engineering Expert with Helm Learning Academy

Master TSQL, Python, Snowflake, Azure & more to build your skillset. Network with Industry Experts & receive reliable career advice & Mentorship.

BENEFITS OF DATA ANALYTICS AND ENGINEERING​

Identify unknown changing
patterns

Data Analytics can reveal low-cost changes to resource management for increased profitability. It will improve the accuracy of predictions by providing better data for predictive modeling and allow organizations to make better decisions about the future and plan for potential risks

Innovate new products and
solutions

Data Analytics can reveal gaps and problems that might be ignored otherwise. Greater insight about purchase decisions, customer feedback and business processes can drive innovation in internal operations of the company and external solutions.

Enhanced Efficiency

Strong integration of data analytics will enhance an organization’s core competencies to open business opportunities and become more effective. Targeted data analytics provide key insights and play an important role in executive decision making, driving business operations to a higher level.

Ask Questions

Please contact our Learning Experts if you have questions or are unsure whether this online Data Analytics and Engineering bootcamp is right for you.

Get Answers

Our experts can give you advice and information on how to make the most of this Data Analytics bootcamp and how to develop your career.

Apply

Seating is limited! Please use the form at the bottom of this page to submit your application.

Learn

Instructor-led training and Q and A sessions feature demonstrations and use case scenarios on a number of topics.

Practice

The best way to learn is to do. We include hands-on projects, quizzes, and other interactive activities to instill the knowledge and skills needed to be fully prepared when you’re hired.

Job Prep

We help you prepare for the job search via mock interviews and resume reviews.

Congratulations!

You’re ready to find a job!

Update Your Resume

Update your resume / CV to emphasize your new knowledge of and experience with Data Analytics and Engineering tools.

Apply for Jobs

You’re ready to apply for positions like Data Scientist, Data Engineer, Machine Learning Engineer, Data Architect, and more!
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Grow your Data Analytics and Engineering Career

Data Analytics and Engineering is the study of data to extract meaningful information, insights and interpretations for businesses.

It is a multidisciplinary approach that cumulates practices and principles from the areas of statistics, mathematics, computer engineering, artificial intelligence and data management to analyze huge volumes of data.

This analysis assists Data Analytics to ask what, how, where, why and when it happened and what will happen, in reference to the data and what can be done with the results

Solve Data Analyst Challenges

Multiple data sources: Generating and analyzing data in different formats from various sources can be difficult and time-consuming.

Identifying and understanding the exact business problem: Working with multiple stakeholders and business managers to define the exact problem can be challenging especially with different teams and people involved.

Elimination of bias: Bias is an imbalance in the training data or prediction model across different groups, such as age or income bracket. Machine Learning enables addressing biases by identifying and measuring them in the data and model.

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DATA ANALYTICS AND ENGINEERING CURRICULUM

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Database Design and Modeling

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Database Programming with SQL Server

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- Database Programming with SQL Server:

This module exposes the candidates to the very details of T-SQL programming along with working on database objects such as tables, views, stored procedures, user defined functions, triggers, & indexes. Module also covers performance implications of multitude of SQL structures such as joins, sub-queries, set operators, CTEs to be incorporated for implementing business requirements

  • Introduction to SQL Server
  • Data Definition Language (DDL)
  • Data Manipulation Language (DML)
  • Data Query Language (DQL)
  • Constraints & Wild Cards
  • Joins & Sub Queries
  • Set Operators & Views
  • Dynamic SQL & Control Flow Statements
  • Stored Procedures, Functions & Triggers
  • Indexes
  • Error Handling
  • Transaction Control Language (TCL)
  • Partitions

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Data Warehouse Design

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- Data Warehouse Design & Architecture:

Description: 

Data Warehousing module provides detailed insight on the designing and developing data structures to store historical data for analytical purposes. This module further explains more about the internals and architecture of Data Warehouses and Data Marts following Kimball or Inmon Methodologies 

Contents: 
  • Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) 
  • Dimension Tables 
  • Fact Tables 
  • Data Warehouse Architecture 
  • INMON and KIMBALL approach  

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Integration Services

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- SQL Server Integration Services (SSIS):

Description: This module talks about SSIS as an ETL tool to practically connect to different data sources, perform various data operations and load data into dimensional structure. Candidates will also be exposed to various concepts of ETL which includes Data Profiling, Data Cleansing, Data Validations, Error Handing and Deployment. 

Contents: 
  • Introduction to SSIS 
  • Data Sources and Connectivity methods 
  • Data Flow and Control Flow 
  • Transformations 
  • Tasks 
  • Containers 
  • Error Handling 
  • Logging 
  • Checkpoints 
  • Transactions 
  • Package and Project Deployment 
  • Incremental Load 

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Analytical Model

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- SQL Server Analysis Service:

Description: Microsoft SQL Server Analysis Services is an online analytical processing and data mining tool in Microsoft SQL Server. SSAS is used as a tool by organizations to analyze and make sense of information possibly spread out across multiple databases, or in disparate tables or files 

Contents: 
  • Introduction to SSAS 
  • Tabular Model 
  • DAX 
  • Perspectives 
  • Key Performance Indicators 
  • Row Level Security 
  • Partitions 
  • Deployment 
  • Processing 

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Reporting Services

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- SQL Server Reporting Service:

Description: This module is mainly used as presentation layer for creating different types of visual reports for making business decisions. Candidates will be learning different types of reporting constructs, publishing, maintaining reports on Report Server. 

Contents: 
  • Introduction to SSRS 
  • Configuration Manager 
  • Report Server and Report Manager 
  • Rendering Reports 
  • Designing Reports 
  • Deploy Reports 
  • Caching 
  • Snapshot 
  • Subscriptions 

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Power BI

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- Reports and Dashboard Design with Power BI:

Description: This module is primarily focused on creating data visualizations, data cleansing, DAX for calculations. In this module candidates will also learn different ways of publishing, embedding, maintaining reports & dashboards within Power BI Desktop and Power BI Service 

Contents: 
  • Introduction to Power BI 
  • Power BI Desktop 
  • Connectivity Methods 
  • Visualizations 
  • Power Query 
  • DAX 
  • Row Level Security 
  • Power BI Service 
  • Gateways 
  • Power BI Licenses 

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Cloud Computing

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- Cloud Computing with MS-Azure:

Description: 

This module focuses on Infrastructure & Platform services which includes compute, storage, Azure data services (Azure SQL, Data Pools, Synapse Analytics and Data Factory). Candidates will learn provisioning and maintaining above mentioned services and understanding the benefits of cloud computing on MS-Azure platform 

Contents: 
  • Introduction to Cloud Computing 
  • Introduction to Azure 
  • Azure Storage 
  • Azure SQL 
  • PowerShell 
  • Data Migration 
  • Azure Data Factory 
  • Azure BI Services 

 

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Snowflake

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- Snowflake:

Description: 

Snowflake is a cloud data warehouse that can store and analyze all your data records in one place. It can automatically scale up/down its compute resources to load, integrate, and analyze data. Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings.

Contents: 
  • Introduction to Snowflake Architecture
  • Snowflake Editions and Pricing Model
  • Resource Monitor and Data Storage (Micro-partitions)
  • Clustering in Snowflake
  • Query History, Caching, Time travel, Fail Safe, Zero Copy Cloning
  • Objects in Snowflake
  • Data Ingestion to Snowflake using SnowSQL, Snowpipe

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Python

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- Python:

Description: Python is a computer programming language used to build websites and software, automate tasks, and conduct data analysis. Python has become a staple in data science, allowing data analysts and other professionals to use the language to conduct complex statistical calculations, create data visualizations, build machine learning algorithms, manipulate and analyze data, and complete other data-related tasks. 

Contents: 
  • Introduction of Python 
  • Data Types and Variables 
  • List, Dictionary, Sets and Tuples 
  • Control Flow Statements 
  • Functions in Python 
  • File Handling 
  • Object Oriented Concepts 
  • Error Handling 
  • Web Scrapping Principles 
  • Graphical User Interface in Python 
  • Numpy, Panda and Matplotlib Library 

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What kinds of roles do Data Analytics & Engineering Bootcamp graduates land?

  • Data Analyst
  • Data Engineer
  • Database Administrator
  • Machine Learning Engineer
  • Data Scientist
  • Data Architect
  • Statistician
  • Business Analyst
  • Data Scientist
  • Data Manager

FAQs

Data Analytics & Engineering Bootcamp is instructor-led training program to offer you meaningful information, insights, and interpretations to upgrade your skills. Our program covers Data Modeling, Database Programming with SQL Server, SSIS (SQL Server Integration Service), SSAS (SQL Server Analysis Service), SSRS (SQL Server Reporting Service), Power BI, Microsoft Azure, Snowflake and Python.

Through our Bootcamp you can easily upgrade your skills, know using the tools required to build great products, and analyze their performance. Data Science & Data Engineering is becoming important because it allows businesses to optimize and secure data.
This role is useful across all industries and gradually it is gaining importance. Also, the earning potential of the Data Engineer or Data Analyst is really high, making it a good career option.