Data and Analytics Strategy for Business: Leverage Data and AI to Achieve Your Business Goals

 

Data and Analytics Strategy for Business: Leverage Data and AI to Achieve Your Business Goals

Introduction: Why Data and Analytics Matter

In today’s digital-first business landscape, organizations are generating massive amounts of data every day. However, data by itself is meaningless unless it is analyzed and applied strategically. A robust data and analytics strategy allows businesses to convert raw information into actionable insights, driving informed decisions, improving operational efficiency, and enhancing customer experiences. When combined with Artificial Intelligence (AI), data analytics becomes a powerful tool that can predict trends, automate processes, and deliver a competitive advantage.

Define Clear Business Objectives

The foundation of any successful data strategy is a clear understanding of business goals. Businesses must ask: What decisions do we want data to support? Examples of objectives include increasing customer retention, optimizing product pricing, reducing operational costs, or improving marketing ROI. Defining specific goals ensures that data collection and analysis efforts are aligned with measurable outcomes that drive business growth.

Assess Data Maturity

Before implementing advanced analytics, it’s crucial to evaluate your current data infrastructure and capabilities. This involves reviewing the quality, accuracy, and accessibility of data, as well as the tools and skills available within the organization. Understanding your data maturity helps prioritize areas for improvement and ensures that analytics initiatives are built on a strong foundation.

Implement Data Governance

Data governance is essential for maintaining data integrity, security, and compliance. Establishing standardized processes for data collection, storage, and management ensures that insights are reliable and actionable. It also ensures compliance with data privacy regulations, protects sensitive information, and reduces the risk of errors in decision-making.

Leverage Advanced Analytics and AI

Modern business strategies leverage AI-powered analytics to go beyond descriptive reporting. Predictive analytics forecasts future trends, prescriptive analytics recommends optimal actions, and machine learning algorithms automate decision-making processes. AI applications, such as Natural Language Processing (NLP), help analyze customer sentiment from reviews and social media, providing deeper understanding of market behavior.

Choose the Right Tools and Platforms

Selecting the right analytics tools and platforms is critical for effective data utilization. Data warehouses and lakes centralize structured and unstructured data, while Business Intelligence (BI) platforms like Tableau, Power BI, or Looker provide visualization and reporting capabilities. AI and machine learning platforms, such as TensorFlow, AWS SageMaker, or Azure AI, enable predictive modeling, automation, and actionable insights at scale.

Promote a Data-Driven Culture

Even with advanced tools, a data strategy fails without a culture that values data-driven decision-making. Organizations should encourage collaboration between business and data teams, train employees to interpret and act on insights, and foster continuous learning. A culture that prioritizes experimentation and evidence-based decisions ensures long-term success of analytics initiatives.

Measure Success with Key Metrics

Tracking the impact of your data strategy is essential. Key performance indicators (KPIs) may include revenue growth, cost savings, customer satisfaction, operational efficiency, and predictive model accuracy. Regularly measuring these metrics helps identify areas of improvement and ensures that analytics efforts are delivering tangible business value.

Real-World Applications of Data and AI

Retail: AI-driven analytics enable personalized recommendations, boosting sales and customer loyalty.

Healthcare: Predictive models optimize hospital staffing, patient flow, and treatment outcomes.

Finance: Machine learning algorithms detect fraudulent transactions in real time.

Manufacturing: Predictive maintenance reduces downtime and increases operational efficiency.

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Conclusion

A strong data and analytics strategy, powered by AI, transforms businesses into proactive, insight-driven organizations. Companies that effectively collect, analyze, and act on data gain a competitive advantage, improve efficiency, and deliver superior customer experiences. In the modern business landscape, leveraging data is no longer optional—it is essential for achieving sustainable growth and success.

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Data Engineering on AWS - Foundations

 

Data Engineering on AWS – Foundations

Introduction

In the era of data-driven decision-making, data engineering has become a cornerstone for building reliable, scalable, and efficient data pipelines. As organizations move to the cloud, AWS (Amazon Web Services) has emerged as a leading platform for building end-to-end data engineering solutions. This blog will walk you through the foundational concepts of Data Engineering on AWS, highlighting core services, architectural patterns, and best practices.

What is Data Engineering?

Data engineering is the practice of designing and building systems to collect, store, process, and make data available for analytics and machine learning. It focuses on the infrastructure and tools that support the data lifecycle—from ingestion and transformation to storage and serving. In the cloud, data engineers work with a variety of managed services to handle real-time streams, batch pipelines, data lakes, and data warehouses.

Why Choose AWS for Data Engineering?

AWS offers a comprehensive and modular ecosystem of services that cater to every step of the data pipeline. Its serverless, scalable, and cost-efficient architecture makes it a preferred choice for startups and enterprises alike. With deep integration among services like S3, Glue, Redshift, EMR, and Athena, AWS enables teams to build robust pipelines without worrying about underlying infrastructure.

Core Components of AWS-Based Data Engineering

1. Data Ingestion

Ingesting data is the first step in any pipeline. AWS supports multiple ingestion patterns:

• Amazon Kinesis – Real-time data streaming from IoT devices, app logs, or sensors
• AWS DataSync – Fast transfer of on-premise data to AWS
• AWS Snowball – For large-scale offline data transfers
• Amazon MSK (Managed Kafka) – Fully managed Apache Kafka service for streaming ingestion
• AWS IoT Core – Ingest data from connected devices

Each tool is purpose-built for specific scenarios—batch or real-time, structured or unstructured data.

2. Data Storage

Once data is ingested, it needs to be stored reliably and durably. AWS provides several options:

• Amazon S3 – The cornerstone of data lakes; stores unstructured or semi-structured data
• Amazon Redshift – A fast, scalable data warehouse optimized for analytics
• Amazon RDS / Aurora – Managed relational databases for transactional or operational storage
• Amazon DynamoDB – NoSQL storage for high-throughput, low-latency access
• AWS Lake Formation – Builds secure, centralized data lakes quickly on top of S3

These services help ensure that data is readily accessible, secure, and scalable.

3. Data Processing and Transformation

After storing data, the next step is transformation—cleaning, normalizing, enriching, or aggregating it for downstream use:

• AWS Glue – A serverless ETL (extract, transform, load) service with built-in data catalog
• Amazon EMR (Elastic MapReduce) – Big data processing using Spark, Hive, Hadoop
• AWS Lambda – Lightweight, event-driven processing for small tasks
• Amazon Athena – Serverless querying of S3 data using SQL
• AWS Step Functions – Orchestration of complex workflows between services

These tools support both batch and real-time processing, giving flexibility based on data volume and velocity.

4. Data Cataloging and Governance

For large data environments, discoverability and governance are critical. AWS provides:

• AWS Glue Data Catalog – Central metadata repository for all datasets
• AWS Lake Formation – Role-based access control and governance over data lakes
• AWS IAM – Enforces fine-grained access permissions
• AWS Macie – Automatically identifies sensitive data such as PII
• AWS CloudTrail & Config – Track access and changes for compliance auditing

Governance ensures that data remains secure, traceable, and compliant with policies like GDPR and HIPAA.

5. Data Serving and Analytics

The end goal of data engineering is to make data usable for analytics and insights:

• Amazon Redshift – Analytical queries across petabyte-scale data
• Amazon QuickSight – Business intelligence dashboards and visualizations
• Amazon OpenSearch (formerly Elasticsearch) – Search and log analytics
• Amazon SageMaker – Machine learning using prepared datasets
• Amazon API Gateway + Lambda – Serve processed data via APIs

These services bridge the gap between raw data and actionable insights.

Benefits of Building Data Pipelines on AWS

Scalability – Elastic services scale with your data

Security – Fine-grained access control and data encryption

Cost-Efficiency – Pay-as-you-go and serverless options

Integration – Seamless connections between ingestion, storage, and processing

Automation – Use of orchestration tools to automate the entire data pipeline

Together, these benefits make AWS an ideal platform for modern data engineering.

Common Architectural Pattern: Modern Data Lake

Here’s a simplified architectural flow:

Data Ingestion via Kinesis or DataSync

Storage in S3 (raw zone)

ETL Processing with AWS Glue or EMR

Refined Data stored back in S3 (processed zone) or in Redshift

Cataloging using Glue Data Catalog

Analytics with Athena, QuickSight, or SageMaker

This pattern allows you to separate raw and transformed data, enabling reprocessing, lineage tracking, and versioning.

Best Practices for Data Engineering on AWS

Use partitioning and compression in S3 for query efficiency

Adopt schema evolution strategies in Glue for changing data

Secure your data using IAM roles, KMS encryption, and VPC isolation

Leverage spot instances and auto-scaling in EMR for cost savings

Monitor and log everything using CloudWatch and CloudTrail

Automate with Step Functions, Lambda, and CI/CD pipelines

Following these best practices ensures high availability, reliability, and maintainability.

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Conclusion

Data engineering is more than moving and transforming data—it’s about building a foundation for intelligent business operations. AWS provides the flexibility, scalability, and security that modern data teams need to build robust data pipelines. Whether you’re just starting or scaling up, mastering these foundational AWS services and patterns is essential for success in the cloud data engineering landscape.

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IBM Relational Database Administrator Professional Certificate

 

 Mastering Databases: A Deep Dive into the IBM Relational Database Administrator Professional Certificate

In the age of big data, cloud computing, and AI, databases remain the backbone of modern technology. From storing customer information to powering real-time applications, relational databases are everywhere. That’s why skilled database administrators (DBAs) are in high demand across industries.

If you’re looking to build a solid career in database management, the IBM Relational Database Administrator (RDBA) Professional Certificate is one of the most comprehensive and industry-aligned programs available online today.

What is the IBM RDBA Professional Certificate?

Offered through Coursera and developed by IBM, this professional certificate program provides learners with job-ready skills to start or advance a career as a relational database administrator.

It’s a self-paced, beginner-friendly specialization designed to equip you with both theoretical knowledge and hands-on experience in administering relational databases using popular technologies like IBM Db2, SQL, and Linux.

Course Structure: What You’ll Learn

The program consists of 6 fully online courses, each taking approximately 3–4 weeks to complete (if studying part-time). Here's a breakdown of what you can expect:

1. Introduction to Data and Databases

Understanding the role of data in the digital world

Types of databases: relational vs. non-relational

Overview of data models and schemas

2. Working with SQL and Relational Databases

Core SQL concepts (SELECT, JOIN, WHERE, GROUP BY, etc.)

Data definition and manipulation (DDL/DML)

Writing and optimizing queries

3. Database Administration Fundamentals

Installing and configuring IBM Db2

Creating and managing database objects (tables, indexes, views)

Backup, recovery, and restore operations

4. Advanced Db2 Administration

Security management and user access controls

Database monitoring and performance tuning

Job scheduling, logs, and troubleshooting

5. Working with Linux for Database Administrators

Navigating the Linux command line

File system structure, permissions, and process control

Shell scripting basics for automation

6. Capstone Project: Database Administration Case Study

Apply your knowledge in a simulated real-world project

Set up and administer a Db2 database instance

Create user roles, automate tasks, optimize queries

Skills You’ll Gain

By completing the IBM RDBA Professional Certificate, you'll develop a robust skill set including:

SQL querying and optimization

Database installation, configuration, and tuning

Backup and recovery strategies

Access control and user management

Scripting with Linux to automate DBA tasks

Working with IBM Db2 – an enterprise-grade RDBMS

These are industry-relevant, practical skills that can immediately be applied in a job setting.

Hands-On Learning with IBM Tools

One of the biggest advantages of this course is the practical exposure:

You'll work directly with IBM Db2, a powerful relational database used in many enterprise systems.

Use IBM Cloud and virtual labs to gain experience without needing to set up your own infrastructure.

Complete interactive labs, quizzes, and real-world case studies to reinforce your learning.

Who Should Take This Course?

This course is designed for:

• Beginners with little or no background in database administration
• Aspiring DBAs, system administrators, or backend developers
• IT professionals transitioning into database roles
• Students or recent graduates seeking a foundational credential

No prior programming or database knowledge is required, but basic computer literacy and comfort with using the internet and command line are recommended.

Certification & Career Impact

Upon completion, learners earn a Professional Certificate from IBM and a verified badge via Coursera, which can be shared on LinkedIn or added to resumes. This can greatly enhance your visibility in the job market.

Career Roles After Completion:

• Junior Database Administrator
• SQL Analyst
• Database Support Engineer
• System Administrator (with DB focus)
• Technical Support Specialist

This certification also builds a foundation for further advancement into roles like Senior DBA, Data Engineer, or Cloud Database Specialist.

Why Choose IBM’s Program?

Here’s why this program stands out:

Industry Credibility – IBM is a global leader in enterprise technology.

Hands-On Learning – Real-world labs with enterprise-grade tools.

Career-Aligned – Focused on job-ready skills and practical application.

Flexible Schedule – 100% online and self-paced.

Affordable – Monthly subscription model (via Coursera) with financial aid available.

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Final Thoughts

As data continues to grow in volume and importance, relational databases remain a critical part of modern infrastructure. By earning the IBM Relational Database Administrator Professional Certificate, you're not just gaining technical skills—you're opening the door to a stable, high-demand career path.

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Data Processing Using Python

Data Processing Using Python: A Key Skill for Business Success

In today's business world, data is generated continuously from various sources such as financial transactions, marketing platforms, customer feedback, and internal operations. However, raw data alone does not offer much value until it is processed into an organized, interpretable form. Data processing is the critical step that transforms scattered data into meaningful insights that support decision-making and strategic planning. Python, thanks to its simplicity and power, has become the preferred language for handling business data processing tasks efficiently.

What is Data Processing?

Data processing refers to the collection, cleaning, transformation, and organization of raw data into a structured format that can be analyzed and used for business purposes. In practical terms, this might include combining monthly sales reports, cleaning inconsistencies in customer information, summarizing financial transactions, or preparing performance reports. Effective data processing ensures that the information businesses rely on is accurate, complete, and ready for analysis or presentation.

Why Choose Python for Data Processing?

Python is particularly well-suited for business data processing for several reasons. Its simple and readable syntax allows even those without a formal programming background to quickly learn and apply it. Furthermore, Python's extensive ecosystem of libraries provides specialized tools for reading data from different sources, cleaning and transforming data, and conducting analyses. Unlike traditional spreadsheet tools, Python scripts can automate repetitive tasks, work with large datasets efficiently, and easily integrate data from multiple formats such as CSV, Excel, SQL databases, and APIs. This makes Python an essential skill for professionals aiming to manage data-driven tasks effectively.

Essential Libraries for Data Processing

Several Python libraries stand out as fundamental tools for data processing. The pandas library offers powerful functions for handling tabular data, making it easy to filter, sort, group, and summarize information. Numpy provides efficient numerical operations and is especially useful for working with arrays and large datasets. Openpyxl focuses on reading and writing Excel files, a format heavily used in many businesses. Other important libraries include csv for handling comma-separated values files and json for working with web data formats. By mastering these libraries, business professionals can greatly simplify complex data workflows.

Key Data Processing Tasks in Python

Reading and Writing Data

An essential first step in any data processing task is reading data from different sources. Businesses often store their data in formats such as CSV files, Excel spreadsheets, or JSON files. Python allows users to quickly import these files into a working environment, manipulate the data, and then export the processed results into a new file for reporting or further use.

Cleaning Data

Real-world data is often imperfect. It can contain missing values, inconsistent formats, duplicates, or outliers that distort analysis. Data cleaning is necessary to ensure reliability and accuracy. Using Python, users can systematically detect and correct errors, standardize formats such as dates and currencies, and remove irrelevant or incorrect entries, laying a solid foundation for deeper analysis.

Transforming Data

Once the data is clean, it often needs to be transformed into a more useful format. This could involve creating new fields such as a "total revenue" column from "units sold" and "price per unit," grouping data by categories such as regions or months, or merging datasets from different sources. These transformations help businesses summarize and reorganize information in a way that supports more effective reporting and analysis.

Analyzing and Summarizing Data

With clean and structured data, businesses can move toward analysis. Python provides tools to calculate descriptive statistics such as averages, medians, and standard deviations, offering a quick snapshot of key trends and patterns. Summarizing data into regional sales performance, customer demographics, or monthly revenue trends helps businesses make informed strategic decisions backed by clear evidence.

What You Will Learn from the Course

By taking this course on Data Processing Using Python, you will develop a strong foundation in handling and preparing business data efficiently. Specifically, you will learn:

The Fundamentals of Data Processing: Understand what data processing means, why it is essential for businesses, and the typical steps involved, from data collection to final analysis.

Using Python for Business Data: Gain hands-on experience with Python programming, focusing on real-world business datasets and practical data problems rather than abstract theory.

Working with Key Python Libraries: Become proficient in popular libraries such as pandas, numpy, openpyxl, and csv, which are widely used in business environments for manipulating, cleaning, and organizing data.

Reading and Writing Different Data Formats: Learn how to import data from CSV, Excel, and JSON files, process it, and export the results for use in reports, dashboards, or presentations.

Real-World Applications in Business

Python's capabilities in data processing extend across different business domains. In finance, Python can automate budget tracking, consolidate expense reports, and even assist in financial forecasting. In marketing, Python scripts can scrape campaign data from social media platforms, clean and organize customer response data, and generate campaign performance summaries. Operations teams can use Python to monitor inventory levels, manage supply chain records, and streamline order processing. Human resources departments might process employee data for payroll and performance evaluations. Across industries, Python transforms raw, chaotic data into clean, actionable intelligence.

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Conclusion

Data processing using Python is a game-changer for businesses aiming to leverage their data effectively. With Python’s simplicity, powerful libraries, and automation capabilities, even non-technical professionals can perform complex data tasks with ease. Mastering these skills not only saves time and improves data accuracy but also empowers businesses to make better, faster, and smarter decisions. As companies continue to move toward a more data-driven future, learning how to process data with Python is not just an advantage — it’s a necessity.

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Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster

 

Printed in Color

Develop an array of effective strategies and blueprints to approach any new data analysis on the Kaggle platform and create Notebooks with substance, style and impact

Leverage the power of Generative AI with Kaggle Models

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

Master the basics of data ingestion, cleaning, exploration, and prepare to build baseline models

Work robustly with any type, modality, and size of data, be it tabular, text, image, video, or sound

Improve the style and readability of your Notebooks, making them more impactful and compelling

Book Description

Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques.

For each topic, we provide one or more notebooks, developing reusable analysis components through Kaggle's Utility Scripts feature, introduced progressively, initially as part of a notebook, and later extracted for use across future notebooks to enhance code reusability on Kaggle. It aims to make the notebooks' code more structured, easy to maintain, and readable.

Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you'll move on to preliminary data analysis, advanced data exploration, feature qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.

What you will learn

Approach a dataset or competition to perform data analysis via a notebook

Learn data ingestion and address issues arising with the ingested data

Structure your code using reusable components

Analyze in depth both small and large datasets of various types

Distinguish yourself from the crowd with the content of your analysis

Enhance your notebook style with a color scheme and other visual effects

Captivate your audience with data and compelling storytelling techniques

Who this book is for

This book is suitable for a wide audience with a keen interest in data science and machine learning, looking to use Kaggle Notebooks to improve their skills and rise in the Kaggle Notebooks ranks. This book caters to:

Beginners on Kaggle from any background

Seasoned contributors who want to build various skills like ingestion, preparation, exploration, and visualization

Expert contributors who want to learn from the Grandmasters to rise into the upper Kaggle rankings

Professionals who already use Kaggle for learning and competing

Table of Contents

Introducing Kaggle and Its Basic Functions

Getting Ready for Your Kaggle Environment

Starting Our Travel - Surviving the Titanic Disaster

Take a Break and Have a Beer or Coffee in London

Get Back to Work and Optimize Microloans for Developing Countries

Can You Predict Bee Subspecies?

Text Analysis Is All You Need

Analyzing Acoustic Signals to Predict the Next Simulated Earthquake

Can You Find Out Which Movie Is a Deepfake?

Unleash the Power of Generative AI with Kaggle Models

Closing Our Journey: How to Stay Relevant and on Top

Hard Copy: Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster

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Finance with Rust: The 2024 Quantitative Finance Guide to - Financial Engineering, Machine Learning, Algorithmic Trading, Data Visualization & More

 

Reactive Publishing

"Finance with Rust" is a pioneering guide that introduces financial professionals and software developers to the transformative power of Rust in the financial industry. With its emphasis on speed, safety, and concurrency, Rust presents an unprecedented opportunity to enhance financial systems and applications.

Written by an accomplished software developer and entrepreneur, this book bridges the gap between complex financial processes and cutting-edge technology. It offers a comprehensive exploration of Rust's application in finance, from developing faster algorithms to ensuring data security and system reliability.

Within these pages, you'll discover:

An introduction to Rust for those new to the language, focusing on its relevance and benefits in financial applications.

Step-by-step guides on using Rust to build scalable and secure financial models, algorithms, and infrastructure.

Case studies demonstrating the successful integration of Rust in financial systems, highlighting its impact on performance and security.

Practical insights into leveraging Rust for financial innovation, including blockchain technology, cryptocurrency platforms, and more.

"Finance with Rust" empowers you to stay ahead in the fast-evolving world of financial technology. Whether you're aiming to optimize financial operations, develop high-performance trading systems, or innovate with blockchain and crypto technologies, this book is your essential roadmap to success.

Hard Copy: Finance with Rust: The 2024 Quantitative Finance Guide to - Financial Engineering, Machine Learning, Algorithmic Trading, Data Visualization & More

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Web Applications and Command-Line Tools for Data Engineering

 

What you'll learn

Construct Python Microservices with FastAPI

Build a Command-Line Tool in Python using Click

Compare multiple ways to set up and use a Jupyter notebook

Join Free: Web Applications and Command-Line Tools for Data Engineering

There are 4 modules in this course

In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale. Finally, you will build a powerful command-line tool to automate testing and quality control for publishing and sharing your tool with a data registry.

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Database Engineer Capstone

 

What you'll learn

Build a MySQL database solution.

Deploy level-up ideas to enhance the scope of a database project.

Join Free: Database Engineer Capstone

There are 4 modules in this course

In this course you’ll complete a capstone project in which you’ll create a database and client for Little Lemon restaurant.

To complete this course, you will need database engineering experience.  

The Capstone project enables you to demonstrate multiple skills from the Certificate by solving an authentic real-world problem. Each module includes a brief recap of, and links to, content that you have covered in previous courses in this program. 

In this course, you will demonstrate your new skillset by designing and composing a database solution, combining all the skills and technologies you've learned throughout this program to solve the problem at hand. 

By the end of this course, you’ll have proven your ability to:

-Set up a database project,

-Add sales reports,

-Create a table booking system,

-Work with data analytics and visualization,

-And create a database client.

You’ll also demonstrate your ability with the following tools and software:

-Git,

-MySQL Workbench,

-Tableau,

-And Python.

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Regression Analysis: Simplify Complex Data Relationships

 

What you'll learn

Investigate relationships in datasets

Identify regression model assumptions 

Perform linear and logistic regression using Python

Practice model evaluation and interpretation

Join Free: Regression Analysis: Simplify Complex Data Relationships

There are 6 modules in this course

This is the fifth of seven courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of variance (ANOVA), and logistic regression.  

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. 

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. 

By the end of this course, you will:

-Explore the use of predictive models to describe variable relationships, with an emphasis on correlation

-Determine how multiple regression builds upon simple linear regression at every step of the modeling process

-Run and interpret one-way and two-way ANOVA tests

-Construct different types of logistic regressions including binomial, multinomial, ordinal, and Poisson log-linear regression models

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Introduction to Probability and Data with R

 

Build your subject-matter expertise

This course is part of the Data Analysis with R Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Introduction to Probability and Data with R

There are 8 modules in this course

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.

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Extract, Transform and Load Data in Power BI

 

What you'll learn

How to set up a data source and explain and configure storage modes in Power BI.

How to prepare for data modeling by cleaning and transforming data.

How to use profiling tools to identify data anomalies.

How to reference queries and dataflows and use the Advanced Editor to modify code. 

Join Free: Extract, Transform and Load Data in Power BI

There are 4 modules in this course

This course forms part of the Microsoft Power BI Analyst Professional Certificate. This Professional Certificate consists of a series of courses that offers a good starting point for a career in data analysis using Microsoft Power BI.

In this course, you will learn the process of Extract, Transform and Load or ETL. You will identify how to collect data from and configure multiple sources in Power BI and prepare and clean data using Power Query. You’ll also have the opportunity to inspect and analyze ingested data to ensure data integrity. 

After completing this course, you’ll be able to: 

• Identify, explain and configure multiple data sources in Power BI  

• Clean and transform data using Power Query  

• Inspect and analyze ingested data to ensure data integrity

This is also a great way to prepare for the Microsoft PL-300 exam. By passing the PL-300 exam, you’ll earn the Microsoft Power BI Data Analyst certification.

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Azure Data Lake Storage Gen2 and Data Streaming Solution

 

What you'll learn

How to use Azure Data Lake Storage to make processing Big Data analytical solutions more efficient. 

How to set up a stream analytics job to stream data and manage a running job

How to describe the concepts of event processing and streaming data and how this applies to Azure Stream Analytics 

How to use Advanced Threat Protection to proactively monitor your system and describe the various ways to upload data to Data Lake Storage Gen 2

Join Free: Azure Data Lake Storage Gen2 and Data Streaming Solution

There are 4 modules in this course

In this course, you will see how Azure Data Lake Storage can make processing Big Data analytical solutions more efficient and how easy it is to set up. You will also explore how it fits into common architectures, as well as the different methods of uploading the data to the data store. You will examine the myriad of security features that will ensure your data is secure. Learn the concepts of event processing and streaming data and how this applies to Azure Stream Analytics. You will then set up a stream analytics job to stream data, and learn how to manage and monitor a running job.

This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services for anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta). You will take a practice exam that covers key skills measured by the certification exam.

This is the ninth course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

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Prepare for DP-203: Data Engineering on Microsoft Azure Exam

 

What you'll learn

How to refresh and test your knowledge of the skills mapped to all the main topics covered in the DP-203 exam.

How to demonstrate proficiency in the skills measured in Exam DP-203: Data Engineering on Microsoft Azure

How to outline the key points covered in the Microsoft Data Engineer Associate Specialization

How to describe best practices for preparing for the Exam DP-203: Data Engineering on Microsoft Azure

Join Free: Prepare for DP-203: Data Engineering on Microsoft Azure Exam

There are 3 modules in this course

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses.​​ In this course, you will prepare to take the DP-203 Microsoft Azure Data Fundamentals certification exam. 

You will refresh your knowledge of how to use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis. You will test your knowledge in a practice exam​ mapped to all the main topics covered in the DP-203 exam, ensuring you’re well prepared for certification success. 

You will also get a more detailed overview of the Microsoft certification program and where you can go next in your career. You’ll also get tips and tricks, testing strategies, useful resources, and information on how to sign up for the DP-203 proctored exam. By the end of this course, you will be ready to sign-up for and take the DP-203 exam.​

This is the last course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

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Microsoft Azure Databricks for Data Engineering

 

What you'll learn

How to work with large amounts of data from multiple sources in different raw formats

How to create production workloads on Azure Databricks with Azure Data Factory

How to build and query a Delta Lake 

How to perform data transformations in DataFrame. How to understand the architecture of an Azure Databricks Spark Cluster and Spark Jobs 

Join Free: Microsoft Azure Databricks for Data Engineering

There are 9 modules in this course

In this course, you will learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud.

You will discover the capabilities of Azure Databricks and the Apache Spark notebook for processing huge files. You will come to understand the Azure Databricks platform and identify the types of tasks well-suited for Apache Spark. You will also be introduced to the architecture of an Azure Databricks Spark Cluster and Spark Jobs. You will work with large amounts of data from multiple sources in different raw formats.  you will learn how Azure Databricks supports day-to-day data-handling functions, such as reads, writes, and queries.

This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services for anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure (beta). You will take a practice exam that covers key skills measured by the certification exam.

This is the eighth course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

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Data Integration with Microsoft Azure Data Factory

 

What you'll learn

How to create and manage data pipelines in the cloud 

How to integrate data at scale with Azure Synapse Pipeline and Azure Data Factory

Join Free: Data Integration with Microsoft Azure Data Factory

There are 8 modules in this course

In this course, you will learn how to create and manage data pipelines in the cloud using Azure Data Factory.

This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services. It is ideal for anyone interested in preparing for the DP-203: Data Engineering on Microsoft Azure exam (beta). 

This is the third course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. 

By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).

Read More