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Predictive Analytics Course (4 Courses, 5+ Projects)
This Predictive Analytics Course includes 4 courses , 5 Projects with 54+ hours of video tutorials and Lifetime access.
You will get to learn the concepts and application of Predictive Modeling tools such as SPSS, Minitab, SAS Enterprise Miner in order to analyze and visualize data using them.
Predictive Modeling using Minitab
Predictive Modeling using SPSS
SAS - Predictive Modeling with SAS Enterprise Miner
Predictive Modeling Training
Project â€“ Logistic Regression using SAS Stat
* One Time Payment & Get Lifetime Access
What you get in this Predictive Analytics Course?
Mobile App Access
About Predictive Analytics Course
|Course||No. of Hours|
|Minitab - Predictive Modeling||16h 11m|
|SPSS - Predictive Modeling using SPSS||13h 37m|
|Predictive Modeling with SAS Enterprise Miner||9h 35m|
|Predictive Modeling Training||2h 2m|
|Logistic Regression using SAS Stat||0m|
|Linear Regression in Python||2h 31m|
|R Practical - Predictive Model for Term Deposit Investment||3h 12m|
|Project - Credit Default using Logistic Regression||3h 9m|
|Project - House Price Prediction using Linear Regression||3h 24m|
|Course Name||Online Predictive Analytics Course|
|Deal||You get access to all videos for the lifetime|
|Hours||54+ Video Hours|
|Core Coverage||Learn the concepts and application of Predictive Modeling tools such as SPSS, Minitab, SAS Enterprise Miner in order to analyze and visualize data using them.|
|Course Validity||Lifetime Access|
|Eligibility||Anyone serious about learning Predictive Analytics and wants to make a career in this Field|
|Pre-Requisites||Basic knowledge about Predictive Analytics would be preferable|
|What do you get?||Certificate of Completion for the course|
|Certification Type||Course Completion Certificates|
|Verifiable Certificates?||Yes, you get verifiable certificates for each4 course, 5 Projects with a unique link. These link can be included in your resume/Linkedin profile to showcase your enhanced skills|
|Type of Training||Video Course – Self Paced Learning|
|System Requirement||1 GB RAM or higher|
|Other Requirement||Speaker / Headphone|
Predictive Analytics Course Curriculum
|Sr No.||Course Name||Time Required||Description|
|1||Project – Predictive Modeling using Minitab||16h 11m||At the starting of the tutorial, we’ll give you a glance or Intro of the Predictive Modeling. The Curriculum then focusses on Minitab and its application to Prediction Modelling where we’ll learn Non-linear regression, Anova and Control charts, Nav process-observation, Descriptive statistics, Features of a T-test, Loan Applicant, etc. The subsequent topic covered in this series is to explain ANOVA with the help of Minitab where we’ll learn chi-test, preference, and pulse rate, diff. Between Growth Plan and Dividend Plan in MF etc. Following this we’ll have a look over some correlation techniques after which we’ll learn about Regression Modeling where we’ll go through all the necessities in it and at last this tutorial ends with the topic Predictive Modeling using MS Excel which help use this technique over excel.|
|2||Project – Predictive Modeling using SPSS||13h 18m||This tutorial starts with the topic Importing Datasets where we’ll learn importing data in xlsx, Xls format, learn about Software menus, Mean Standard Deviation, and implementation using SPSS. The next topic in the tutorial is about the correlation techniques which explain its theory, Interpretation, Simple Scatter Plot, Heart Pulse, Statistics Viewer, etc. The next topic will be Linear Regression Modelling where we’ll learn its usage with SPSS, Stock Return, Regression Equation, Copper Expansion, Energy Consumption, Debt Assessment, etc. Following this, we’ll learn about Multiple Regression Modelling, Important Output Variables, etc. In the end we’ll go through Logistic and Multinomial Regression techniques.|
|3||Project on SAS – Predictive Modeling with SAS Enterprise Miner||9h 35m||This predictive modeling training is 9.5 hours long and is quite extensive. It covers topics such as PM SAS EM Introduction, PM SAS EM variable selection, SAS PM EM combination, SAS PM EM neural network, and SAS PM EM regression. It starts with an introduction to SAS and then gradually move towards topics such as selecting SAS tables, creating input data nodes, decision tree in SAS, creating score model, ROC chart, Neural network training, regression-table effect to name a few. This module covers everything that SAS includes for predictive modeling.|
|4||Predictive Modeling Training||2h 2m||This predictive modeling training is more than 2 hours long and here students learn about the introduction to predictive modeling, variables and its definition, steps involved in predictive modeling, smoothing methods, regression algorithms, clustering algorithms, neural network, and support vector machines. Each concept is covered with enough examples and practice exercises. Basics of statistics and data visualization are also covered. Special emphasis is given to data preprocessing, data preparation, model evaluation, and deployment. Data distribution, data plotting, and charts, correlation vs causation, model interpretation, model improvement, etc. are also covered in this module.|
|5||Project – Logistic Regression using SAS Stat||4h 33m||This Video tutorial starts the introduction to the project explaining all its goals and perspective. Following this, we’ll learn Logistic Regression Demonstration where we’ll go through concepts such as Missing Value Imputation, etc. Next, we’ll learn Categorical Inputs following concepts such as Variable Clustering, Variable Screening, etc.|
|6||Project – Linear Regression in Python||2h 15m||This Predictive Analytics Training starts the introduction to the project explaining all its goals and perspective. Next, we’ll learn about the use case for the project, what libraries are important for the project would be determined and imported along with Graphical Univariate Analysis. At last, we’ll learn Linear Regression Boxplot and Outliers.|
|7||Project on R – Predictive Model for Term Deposit Investment||3h 12m||This Video tutorial starts the introduction to the project explaining all its goals and perspective. Next, we’ll learn about the Problem Statement, the Variable explanation, EDA and its insights which will cover data imputation and variable selection, we’ll go through the Model Development, its parameters and the Improvement involved. At last, we’ll take a glance at Model Validation along with its deployment.|
|8||Project – Credit Default using Logistic Regression||3h 9m||This Video tutorial starts the introduction to the project explaining all its goals and perspective. Further, we’ll discuss the Project Steps and files needed to be imported. Next, we’ll learn about Data Processing EDA along with concepts such as the Splitting Data and Confusion Matrix. In Continuation, we’ll go through the topic Hyper Parameter Tuning and end the video with the concept of Decision Tree, its theory, and code explanation.|
|9||Project – House Price Prediction using Linear Regression||2h 8m||This Video tutorial starts the introduction to the project explaining all its goals and perspective. Further, we’ll discuss data preprocessing, its transformation, and target variable splitting. Next to this, we’ll learn about Dataset, Feature Engineering, Handling Missing Values, Correlation, and at last Predicting the Result and Calculating Variance Inflation.|
Predictive Analytics Course – Certificate of Completion
What is Predictive Analytics?
Predictive analytics can be defined as the enactment of withdrawing information from existing data sets to predict or determine the trends/patterns and predict future outcomes based on these trends. Predictive analytics is not the study of astrology which lets you know what will happen shortly. Rather, based on the previous statistics data it forecasts the future outcomes of any process with an admissible level of reliability and it also includes the risk scenarios and their assessments. Predictive analysis and models based on it are typically utilized to forecast future possibilities. When applied to business, predictive models are highly useful as they help to analyze the historical facts and current data which further helps to better understand partners, customers & the products and to recognize the opportunities and the potential risks for a company. It uses several techniques, including machine learning, statistical modeling, and data mining to help analysts make better future business forecasts.
Which Skills will you learn in this Training?
Along with the Predictive Analytics Training, we’ll get used to many new skills and techniques which help in improving your knowledge base and add weight to your CV. Going along the course you will get to taste many of the mathematical and Physics formulas which are used to define many scientific events such as Chi-square test, Linear Regression, etc. Further, we’ll get to know more about data mining, text analytics, and predictive modeling, learn more about terms such as Risk assessment and Fraud detection, and also we’ll get to know the data cycle defined in stages as data preprocessing, data preparation, model evaluation, and deployment. Programming skills, as well as Analytic skills, are also attained while going through the curriculum of the course. Hence, this Predictive Analytics Course gives you full exposure to the world of predictive analytics and help you step into its world.
- As such, there is no prerequisite for learning from this Predictive Analytics Training, but knowledge of certain technologies and ideas will be helpful while learning this technique.
- An individual should have a keen interest in the field of data science.
- He/She should have a strong grasp of inferential and descriptive statistics.
- Individuals should be familiar with programming languages such as Python and R as well as coding experience in these languages will be quite helpful in the course.
- He/She should have a strong understanding of the basic programming concepts such as variables, conditions, loops, functions, and some basic data structures such as arrays, objects, lists, etc.
- You should be self-driven and always motivated to learn. Participation in this Predictive Analytics Training requires consistently meeting deadlines and you should be devoting at least 10 hours per week to this Course.
- Students: Pursuing his diploma, graduation, or degree in the field of statistics, mathematical modeling, Predictive Analytics, etc. can choose out his way to take up this Predictive Analysis course with all the key concepts and fundamentals being explained in this Predictive Analytics Course.
- Professionals: Who wants to become a Data Analytics, Statistician, or an IT professional looking for a switch in his career in the field of Data Analysis and Predictive Modelling. A research scientist presupposed in Data Analysis, IT specialists, professors, and teachers can opt for this Predictive Analysis Course.
- Beginners: Who has an interest in data and needs to learn to use this data for predictive analysis for predicting future outcomes can go for this Predictive Analysis Course which will provide you a great level of understanding of this technology and its usage through the real-life projects given in the Predictive Analytics Training.
Predictive Analytics Course – FAQ’s
Why you should learn Predictive Analysis?
For all the unknown, Predictive Analytics is a method where we use different statistical techniques to use past data to predict informed guesses about future outcomes. Many Multi-National Corporations have been using this technique for years to assess their risk and for detecting fraud. Many Marketing Giants are using this predictive analytical skill to forecast the demands for their products or services and personalize their content accordingly. Examples of companies are Netflix, Amazon, etc.
Why you should take up this online Predictive Analytics Training?
With the growing technology market, the demand for this skill is being increased daily as nowadays every sector such as e-commerce, Insurance, Job Portals, stock market, search engines, etc. All these sectors consume the data of their users to predict the future demand for their products or services. Hence, there is always a need for professionals with predictive analysis knowledge in these companies. Our course offers you all the insights into this skill along with leading examples for better understanding.
- With billions of people accessing the internet every second, petabytes of data are being analyzed ever minute to predict the future demands of the customers. There is a huge competitive growth being registered between companies because of new players introduced in the market. Hence, companies are using the Predictive Analysis technique to find the demand for a product over which they act accordingly to increase their margins. With this demand, there comes a great salary package for newbies joining this field as well as there is a lot to learn in this field which will help you grow rapidly in your career.
Lee Tze Hui
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