In the course students learn or get a reminder about main statistical parameters for data and big data analysis. Students learn how they can activate the Analysis Package in the  Microsoft Excel, and learn and have labs for a lot of functions and methods of this package, such as: - Simple statistics: average, median, dispersion, - Correlation: Pearson coefficient, Fisher criteria - Student criteria, T-functions - Regressions: linear regression, trends.

Learn how to define a Pentaho Kafka Producer and Consumer to implement a quick solution to derive insights. This course is accompanied with a demo project related to banking domain and as a student of this course, you will get practical application of how Apache Kafka and Pentaho can be used in implementing a real time data streaming solution to discover the market demand for loan or total page visit count in real time. Content and Overview Through this course, comprising of several lectures with English subtitles / English captions, Quiz chapters, along with additional resources, you will Understand what is, when and how to carry out realtime data processing solution Gain confidence in implementing such realtime data processing solution using Pentaho and Kafka You can test the knowledge gained through the sessions by attending quizzes and every use case mentioned in the course are explained with demo sessions thereby enabling you to practice the newly learned skills. I will add more contents to this course as and when possible. Downloadable Resources You can download the Pentaho transformations used during the demo sessions (attached as part of a resource material in a lecture of this course), to practice at your end. Learners who complete this course will gain the knowledge and confidence to implement a realtime data streaming solution with Pentaho and Apache Kafka in the projects.

This course help you have skill on data analysis and visualization to start your job on data science and data analysis. You will learn following skill in this course : Loading data Overview on data Selecting data Sorting data Filter data Aggregation function Groupby Apply Merge Visualization Saving data Clean up Rename column Drop column Handle missing data Handle duplicate data Modify data Time series Convert column to date time Select time series data Resampling Hand on with Google App data set Hand on with Ted Talk data set Hand on with Fifa19 data set Take this course and start your first step on data science , data analysis adventure.

Data Analysts aim to discover how data can be used to answer questions and solve problems through the use of technology. Many believe this will be the job of the future and be the single most important skill a job application can have in 2020. In the last two decades, the pervasiveness of the internet and interconnected devices has exponentially increased the data we produce. The amount of data available to us is Overwhelming and Unprecedented. Obtaining, transforming and gaining valuable insights from this data is fast becoming the most valuable and in-demand skill in the 21st century. In this course, you'll learn how to use Data, Analytics, Statistics, Probability, and basic Data Science to give an edge in your career and everyday life. Being able to see through the noise within data, and explain it to others will make you invaluable in any career. We will examine over 2 dozen real-world data sets and show how to obtain meaningful insights. We will take you on one of the most up-to-date and comprehensive learning paths using modern-day tools like Python, Google Colab and Google Data Studio. You'll learn how to create awesome Dashboards, tell stories with Data and Visualizations, make Predictions, Analyze experiments and more! Our learning path to becoming a fully-fledged Data Analyst includes: The Importance of Data Analytics Python Crash Course Data Manipulations and Wrangling with Pandas Probability and Statistics Hypothesis Testing Data Visualization Geospatial Data Visualization Story Telling with Data Google Data Studio Dashboard Design - Complete Course Machine Learning - Supervised Learning Machine Learning - Unsupervised Learning (Clustering) Practical Analytical Case Studies Google Data Studio Dashboard & Visualization Project: Executive Sales Dashboard (Google Data Studio) Python, Pandas & Data Analytics and Data Science Case Studies: Health Care Analytics & Diabetes Prediction Africa Economic, Banking & Systematic Crisis Data Election Poll Analytics Indian Election 2009 vs 2014 Supply-Chain for Shipping Data Analytics Brent Oil Prices Analytics Olympics Analysis - The Greatest Olympians Home Advantage Analysis in Basketball and Soccer IPL Cricket Data Analytics Predicting the Soccer World Cup Pizza Resturant Analytics Bar and Pub Analytics Retail Product Sales Analytics Customer Clustering Marketing Analytics - What Drives Ad Performance Text Analytics - Airline Tweets (Word Clusters) Customer Lifetime Values Time Series Forecasting - Demand/Sales Forecast Airbnb Sydney Exploratory Data Analysis A/B Testing

This course prepares participants to review, analyze, and make decisions based on results from business intelligence projects. The course covers reading and interpreting regression analysis, and gives participants the skills to critically analyze and identify potential limitations on analysis. The course also explores how to predict changes in business outcomes based on analysis and identifying the level of certainty or confidence around those predictions. This paves the way for future detailed courses in predictive analytics. If you would like Continuing Education Credit (e.g. CPE, CE, CPD, etc.) for this course, it is available if you take this course on the Illumeo dot com platform under course title: Business Intelligence – Applying and Using Data Analysis . Illumeo is certified to provide CPE in over two dozen different professional certifications covering finance, accounting, treasury, internal audit, HR, and more. However, in order to receive CPE credit the courses must be taken on an ‘approved-by-the-governing-body’ CPE platform, and for over two dozen corporate professional certifications, that is the Illumeo platform. Go to Illumeo dot com to learn more.

The Problem Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist. And how can you do that? Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming) Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture The Solution Data science is a multidisciplinary field. It encompasses a wide range of topics. Understanding of the data science field and the type of analysis carried out Mathematics Statistics Python Applying advanced statistical techniques in Python Data Visualization Machine Learning Deep Learning Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is. So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2021. We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place. Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save). The Skills 1. Intro to Data and Data Science Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean? Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science. 2. Mathematics Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail. We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on. Why learn it? Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal. 3. Statistics You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist. Why learn it? This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist. 4. Python Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning. Why learn it? When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language. 5. Tableau Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science. Why learn it? A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers. 6. Advanced Statistics Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail. Why learn it? Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section. 7. Machine Learning The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow. Why learn it? Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines. ***What you get*** A $1250 data science training program Active Q&A support All the knowledge to get hired as a data scientist A community of data science learners A certificate of completion Access to future updates Solve real-life business cases that will get you the job You will become a data scientist from scratch We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it. Why wait? Every day is a missed opportunity. Click the “Buy Now” button and become a part of our data scientist program today.

PLEASE READ BEFORE ENROLLING: 1.) THERE IS AN UPDATED VERSION OF THIS COURSE: "PYTHON FOR DATA SCIENCE AND MACHINE LEARNING BOOTCAMP" 2.) IF YOU ARE A COMPLETE BEGINNER IN PYTHON-CHECK OUT MY OTHER COURSE "COMPLETE PYTHON MASTERCLASS JOURNEY"! CLICK ON MY PROFILE TO FIND IT. (PLEASE WATCH THE FIRST PROMO VIDEO ON THIS PAGE FOR MORE INFO) ********************************************************************************************************** This course will give you the resources to learn python and effectively use it analyze and visualize data! Start your career in Data Science! You'll get a full understanding of how to program with Python and how to use it in conjunction with scientific computing modules and libraries to analyze data. You will also get lifetime access to over 100 example python code notebooks, new and updated videos, as well as future additions of various data analysis projects that you can use for a portfolio to show future employers! By the end of this course you will: - Have an understanding of how to program in Python. - Know how to create and manipulate arrays using numpy and Python. - Know how to use pandas to create and analyze data sets. - Know how to use matplotlib and seaborn libraries to create beautiful data visualization. - Have an amazing portfolio of example python data analysis projects! - Have an understanding of Machine Learning and SciKit Learn! With 100+ lectures and over 20 hours of information and more than 100 example python code notebooks, you will be excellently prepared for a future in data science!

This is an introductory course designed to help business professionals and others learn predictive analytic skills that can be applied in a business setting. Since it is designed for business professionals it doesn't delve too deeply into the mathematics of the statistical models. We do the following case studies on Rapidminer software: B2B Churn of an office supply distributor, Market Basket Analysis of a retail computer store, Customer Segmentation of a customer database and Direct Marketing. The following models are used: Linear Regression, Logistic Regression, Association Rules, K-means Clustering and Decision Trees. Through these practical case studies we generate actionable business insights!

Data analysis is critical in business. Get ahead in your career with this important skill. Management depends on decision making and problem solving.   They depend on analytical findings. Not only do we need good sources of data, but we need skills that allow us to interpret and report the results. Discover techniques and best practices for analysis by learning the analytical process.

This course helps you learn simple but powerful ways to work with data. It is designed to be help people with limited statistical or programming skills quickly become productive in an increasingly digitized workplace. In this course you will use R (an open-sourced, easy to use data mining tool) and practice with real life data-sets. We focus on the application and provide you with plenty of support material for your long term learning. It also includes a project that you can attempt when you feel confident in the skills you learn.