4124 Analyst courses

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Architects and operators who build and manage data analytics pipelines Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a batch data analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Introduction to Amazon EMR Using Amazon EMR in analytics solutions Amazon EMR cluster architecture Interactive Demo 1: Launching an Amazon EMR cluster Cost management strategies Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage Storage optimization with Amazon EMR Data ingestion techniques Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR Apache Spark on Amazon EMR use cases Why Apache Spark on Amazon EMR Spark concepts Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell Transformation, processing, and analytics Using notebooks with Amazon EMR Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive Using Amazon EMR with Hive to process batch data Transformation, processing, and analytics Practice Lab 2: Batch data processing using Amazon EMR with Hive Introduction to Apache HBase on Amazon EMR Module 5: Serverless Data Processing Serverless data processing, transformation, and analytics Using AWS Glue with Amazon EMR workloads Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions Module 6: Security and Monitoring of Amazon EMR Clusters Securing EMR clusters Interactive Demo 3: Client-side encryption with EMRFS Monitoring and troubleshooting Amazon EMR clusters Demo: Reviewing Apache Spark cluster history Module 7: Designing Batch Data Analytics Solutions Batch data analytics use cases Activity: Designing a batch data analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

The Mindset, Culture, and Muscles Of Business Agility The world is changing more rapidly than ever before. Organizations of every size are overwhelmed by the pace of change and are being constantly challenged to understand & react to the major disruptions in the marketplace and in their organizations. High-performing and adaptable organizations that leverage, lead, and thrive in this ambiguous and unpredictable market will be in the best position to win with their customers. We call this business agility; the set of organizational capabilities, behaviors, and ways of working that affords your business the freedom & flexibility to achieve its purpose - no matter what the future brings. In this talk, Ahmed Sidky will highlight the mindset and culture necessary to endure the journey of improving your company's Business Agility. Business Agility is not something an organization has or doesn't, it is more like fitness or athletic performance, your organization can increase its agility with intentional, deliberate, and disciplined effort. So using the Domains of Business Agility (published by the Business Agility Institute) Ahmed will explain the 12 different muscles that an organization needs to exercise in order to increase its agility. What You Will Learn: A deeper understanding of agility as it pertains to the entire organization not just a project delivery framework The essential relationship between business agility and customer-centricity 12 key areas of focus to increase business agility - grouped into 4 groups (Leadership, Individuals, Operations and Enablers) People are the most powerful and agile part of any organization. People ignite and sustain business agilit

Toyota Kata - What is it. Where Might it Fit. and Why? Future solutions are getting less and less likely to be ones we've used before. And even if we can draw on 'experience', it may represent the lowest form of innovation which may not be good enough; we are limiting ourselves to only what we know now.Agile is about mindset, not about process. Mindset can support agile behaviors or can hinder them. Improvement exercises involve multiple people or groups working on different parts of the puzzle; how they understand customer needs and how they collaborate to create a cohesive whole can greatly influence success. So the most important question becomes, 'How might we develop an Agile mindset?'The connection between Agile and Toyota Kata is intriguing. When you look into why, it's actually quite plain and simple. Further, when you have an understanding of Toyota Kata, how it came about and why, then its potential value to those adopting Agile shines through even more so. You will recognize the potential of the 2 unintentionally hidden managerial routines at Toyota from which 'Toyota Kata' was born."One of the best, compact introductions to Toyota Kata that I've seen!" (Quote by Mike Rother, author of 'Toyota Kata', following a presentation by Oscar in Austin TX in Feb 2020.) In this session you will: See the connection between Agile and Toyota Kata and their common ancestry See why the most success in the future will come to those who can rapidly adapt and innovate Understand that a concept or model alone generally won't change behaviors (and why), but a practice routine will Understand the 2 'Kata' patterns and get a feel for how you can start your application of them, one in particular

Toyota Kata - What is it. Where Might it Fit. and Why? Future solutions are getting less and less likely to be ones we've used before. And even if we can draw on 'experience', it may represent the lowest form of innovation which may not be good enough; we are limiting ourselves to only what we know now.Agile is about mindset, not about process. Mindset can support agile behaviors or can hinder them. Improvement exercises involve multiple people or groups working on different parts of the puzzle; how they understand customer needs and how they collaborate to create a cohesive whole can greatly influence success. So the most important question becomes, 'How might we develop an Agile mindset?'The connection between Agile and Toyota Kata is intriguing. When you look into why, it's actually quite plain and simple. Further, when you have an understanding of Toyota Kata, how it came about and why, then its potential value to those adopting Agile shines through even more so. You will recognize the potential of the 2 unintentionally hidden managerial routines at Toyota from which 'Toyota Kata' was born."One of the best, compact introductions to Toyota Kata that I've seen!" (Quote by Mike Rother, author of 'Toyota Kata', following a presentation by Oscar in Austin TX in Feb 2020.) In this session you will: See the connection between Agile and Toyota Kata and their common ancestry See why the most success in the future will come to those who can rapidly adapt and innovate Understand that a concept or model alone generally won't change behaviors (and why), but a practice routine will Understand the 2 'Kata' patterns and get a feel for how you can start your application of them, one in particular

Compass for Agility - Leveraging Agility for Business Problem-Solving Join Leila Rao as she introduces the Compass for Agility. This 90-day iterative approach is a pragmatic and personalized method, enabling organizations to build the capacity and culture for responding to challenges and opportunities. This provides a structure for organizations to explore what'ss working and what isnTt in their ecosystem to successfully and sustainably build the capability for continuous learning and improvement. The Compass helps with addressing "wicked" business problems set apart by one key variation: agile is the how, not the "why". Enhancing business agility is a fortunate by-product of directly resolving pain points because agile serves as the means, not the end. Empower your organization to orient itself by its unique needs and values so that you can continually create customized maps as you chart your own journey in volatile and uncertain terrains.

Inspiring The Human-Centered Organization - Three Laws of Business Agility In his keynote, Steve Denning will take you along on his journey in which he first encountered the success of the agile approach in software development and witnessed how this innovative method slowly but surely became the norm in many large multinationals. As Steve got more involved in the agile method, he and other members of the SD Learning Consortium set out on a quest to find out what is real agile, and what is fake? What was successful, and what was not? They found that what was needed is an Agile Mindset. It's a different way of looking at, thinking about, understanding and interacting with the world. In relation to this mindset, they identified what he calls the 'Three Business Laws of Agile'. During his presentation, Steve will explain these laws in depth, helping you to understand how you too can adopt this Agile Mindset yourself. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs. Each professional development activity yields one PDU for one hour spent engaged in the activity. Some limitations apply and can be found in the Ways to Earn PDUs section that discusses PDU activities and associated policies.

Agile at Tesla - The Misinformation That You Can't Apply Agile to Hardware What is it like to work in 3 hour sprints? How does any company deploy 27 changes per week in hardware, or more? What is a team size like when you are moving that fast? What about certification, and how does testing fit in such short sprints? Key Takeaways: Learn how increase speed, quality, and efficiency at the same time. Understand how to increase happiness, willingness, respect, and enthusiasm in engineering and manufacturing culture. Find out about Joes complete New Product Development and New Product Introduction outline for agile hardware

This course was developed for one of the largest investment institutions in the world, a multi-trillion household name. We explain in detail our tips and tricks to build an accurate and rolling enterprise value, and then review different valuation methodologies, from DCF, through the sum of the parts and football field analyses to LTV/CAC based methods. We conclude with a series of case studies examining the valuation of individual stocks.

Overview This comprehensive course on Python for Data Analysis will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Python for Data Analysis comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is this course for? There is no experience or previous qualifications required for enrolment on this Python for Data Analysis. It is available to all students, of all academic backgrounds. Requirements Our Python for Data Analysis is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 19 sections • 99 lectures • 00:08:00 total length •Welcome & Course Overview: 00:07:00 •Set-up the Environment for the Course (lecture 1): 00:09:00 •Set-up the Environment for the Course (lecture 2): 00:25:00 •Two other options to setup environment: 00:04:00 •Python data types Part 1: 00:21:00 •Python Data Types Part 2: 00:15:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00 •Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00 •Python Essentials Exercises Overview: 00:02:00 •Python Essentials Exercises Solutions: 00:22:00 •What is Numpy? A brief introduction and installation instructions.: 00:03:00 •NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00 •NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00 •NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00 •NumPy Essentials Exercises Overview: 00:02:00 •NumPy Essentials Exercises Solutions: 00:25:00 •What is pandas? A brief introduction and installation instructions.: 00:02:00 •Pandas Introduction: 00:02:00 •Pandas Essentials - Pandas Data Structures - Series: 00:20:00 •Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00 •Pandas Essentials - Handling Missing Data: 00:12:00 •Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00 •Pandas Essentials - Groupby: 00:10:00 •Pandas Essentials - Useful Methods and Operations: 00:26:00 •Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00 •Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00 •Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00 •Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00 •Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00 •Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00 •Matplotlib Essentials - Exercises Overview: 00:06:00 •Matplotlib Essentials - Exercises Solutions: 00:21:00 •Seaborn - Introduction & Installation: 00:04:00 •Seaborn - Distribution Plots: 00:25:00 •Seaborn - Categorical Plots (Part 1): 00:21:00 •Seaborn - Categorical Plots (Part 2): 00:16:00 •Seborn-Axis Grids: 00:25:00 •Seaborn - Matrix Plots: 00:13:00 •Seaborn - Regression Plots: 00:11:00 •Seaborn - Controlling Figure Aesthetics: 00:10:00 •Seaborn - Exercises Overview: 00:04:00 •Seaborn - Exercise Solutions: 00:19:00 •Pandas Built-in Data Visualization: 00:34:00 •Pandas Data Visualization Exercises Overview: 00:03:00 •Panda Data Visualization Exercises Solutions: 00:13:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00 •Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00 •Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:37:00 •Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00 •Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00 •Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00 •Introduction to ML - What, Why and Types..: 00:15:00 •Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00 •scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00 •Good to know! How to save and load your trained Machine Learning Model!: 00:01:00 •scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00 •scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00 •Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00 •scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00 •scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00 •Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00 •scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00 •scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00 •scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00 •Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00 •scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00 •scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00 •scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00 •Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00 •scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00 •scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00 •scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00 •scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00 •Theory: K Means Clustering, Elbow method ..: 00:11:00 •scikit-learn - K Means Clustering - Hands-on: 00:23:00 •scikit-learn - K Means Clustering (Project Overview): 00:07:00 •scikit-learn - K Means Clustering (Project Solutions): 00:22:00 •Theory: Principal Component Analysis (PCA): 00:09:00 •scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00 •scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00 •Theory: Recommender Systems their Types and Importance: 00:06:00 •Python for Recommender Systems - Hands-on (Part 1): 00:18:00 •Python for Recommender Systems - - Hands-on (Part 2): 00:19:00 •Natural Language Processing (NLP) - (Theory Lecture): 00:13:00 •NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00 •NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00 •NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00 •NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00 •NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00 •Resources- Python for Data Analysis: 00:00:00

Overview This comprehensive course on Clinical Data Analysis with SAS will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Clinical Data Analysis with SAS comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Clinical Data Analysis with SAS. It is available to all students, of all academic backgrounds. Requirements Our Clinical Data Analysis with SAS is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 5 sections • 30 lectures • 01:54:00 total length •Course Promo: 00:01:00 •1.1 Components of the Pharma Industry: 00:05:00 •1.2 Phases of Clinical Trials: 00:06:00 •1.3 Data and Reports in Clinical Trials: 00:04:00 •1.4 Types of Data: 00:05:00 •2.1 Clinical Study Protocol: 00:02:00 •2.2 Ethical Consent: 00:01:00 •2.3 Inclusion-Exclusion Criteria: 00:01:00 •2.4 Statistical Analysis Plan: SAP, Mockshell and CRF: 00:04:00 •3.1 General SAS Programming Steps: 00:02:00 •3.2 One Search Report: Demographics Table: 00:04:00 •3.3 Understanding the Demographics Table: 00:03:00 •3.4 Programming the Demographics Table: 00:05:00 •3.5 Importing Raw Demographic Data into the SAS: 00:04:00 •3.6 Deciding what Procedure to Use: 00:02:00 •3.7 Deriving the AGE variable: 00:10:00 •3.8 Obtaining Summary Statistics for AGE: 00:04:00 •3.9 Adding the 3rd Treatment Group using Explicit Output: 00:05:00 •3.10 Deriving the SEX variable: 00:03:00 •3.11 Obtaining Summary Statistics for SEX: 00:03:00 •3.12 Concatenating the COUNT and PERCENT Variables: 00:03:00 •3.13 Deriving the RACE Variable: 00:03:00 •3.14 Obtaining Summary Statistics for RACE: 00:03:00 •3.15 Stacking All the 3 Summary Statistics Together: 00:06:00 •3.16 Fixing the Precision Points: 00:04:00 •3.17 Transposing Data: 00:03:00 •3.18 Fixing the Order of Statistical Parameters: 00:05:00 •3.19 Building the Final Report: 00:02:00 •3.20 Putting the Final Touches to the Report: 00:11:00 •Resources - Clinical Data Analysis with SAS: 00:00:00