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PREDICTIVE MODELING with PYTHON Course Bundle - 8 Courses in 1
This Predictive Modeling with Python Course includes 8 Courses with 23+ hours of video tutorials and Lifetime access. You will get to learn how to analyze and visualize data using Python libraries.
* One Time Payment & Get Lifetime Access
What you get in this PREDICTIVE MODELING with PYTHON Course Bundle - 8 Courses in 1?
23+ Hours
8 Courses
Course Completion Certificates
Lifetime Access
Self-paced Courses
Technical Support
Mobile App Access
Case Studies
About PREDICTIVE MODELING with PYTHON Course Bundle
| Courses | You get access to all videos for the lifetime |
| Hours | 23+ Video Hours |
| Core Coverage | Learn how to analyze and visualize data using Python libraries. |
| Course Validity | Lifetime Access |
| Eligibility | Anyone who is serious about learning predictive modeling and wants to make a career in the data analytics field |
| Pre-Requisites | Basis Statistical concepts and predictive modeling knowledge |
| What do you get? | Certificate of Completion for the course |
| Certification Type | Course Completion Certificates |
| Verifiable Certificates? | Yes, you get verifiable certificates for each8 course, 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 |
PREDICTIVE MODELING with PYTHON Course Bundle Curriculum
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MODULE 1: Statistics Essentials Training
Courses No. of Hours Certificates Details Predictive Analytics and Modeling with Python 8h 27m ✔ Machine Learning Python Case Study - Diabetes Prediction 1h 02m ✔ Linear Regression & Supervised Learning in Python 2h 28m ✔ Financial Analytics with Python 1h 6m ✔ -
MODULE 2: Logistic Regression & Predictive Modeling
Courses No. of Hours Certificates Details Logistic Regression & Supervised Machine Learning in Python 2h 6m ✔ Predicting Credit Default using Logistic Regression in Python 3h 3m ✔ House Price Prediction using Linear Regression in Python 3h 2m ✔ Sales Forecasting using Time Series Analysis in Python 2h 13m ✔
| Serial No | Course Name | Duration | Description |
| 1 | Predictive Modeling with Python | 9h 44m | In this module, you will get an introduction to Predictive Modelling with Python. You will be guided through the installation of the required software. Data Pre-processing, which includes Data frame, splitting dataset, feature scaling, etc. You will gain an edge on Linear Regression, Salary Prediction, Logistic Regression. You will get to work on various datasets dealing with Credit Risk and Diabetes. |
| 2 | Machine Learning with Python Project – Predict Diabetes on Diagnostic Measures | 1h 07m | In this section, you will work on Pima Indians Diabetes using Machine Learning. You will be guided through the installation and will have practical lessons on Pima Classification, Splitting Dataset, Checking the ROC. |
| 3 | Project – Linear Regression in Python | 2h 15ms | You will be introduced to Linear Regression in Python in depth in this module. You will be learning about the use case and libraries and also regarding the graphical univariate analysis. Along with that, you will be taught Boxplot, Bivariate Analysis, etc. |
| 4 | Project on Python Data Science – Predicting the Survival of Passenger in Titanic | 2h 11m | Here you will learn about Import Libraries, Decision Tree Classifiers, Logistic Regression, Load libraries, bar plot, modeling, training set, etc. |
| 5 | Financial Analytics with Python | 2h 11m | This section emphasizes on the use of Python Libraries and the working of the Data Frames. In-depth study of Analytics and Financial Time series analysis along with data visualization, financial plots, and 3D Charts. |
| 6 | Project – Credit Default using Logistic Regression | 3h 9m | You will explain in detail about the project, the files that need to be imported, data pre-processing, splitting data, and confusion matrix. Topics like Hyper Parameter Tuning, Decision Tree Theory, Installation of Graph viz and Pydotplus, etc |
| 7 | Project – House Price Prediction using Linear Regression | 2h 8m | This project helps you to focus on coding feature engineering, handling missing values, exploratory data analysis, calculation variation inflation factor, etc. |
| 8 | Forecasting the Sales using Time Series Analysis in Python | 2h 29 m | This project emphasizes and will give you more insight into data processing and feature engineering along with graph visualization components. |
Predictive Modeling with Python Course – Certificate of Completion
What is Predictive Modeling with Python?
It is the use of data and statistics to predict the outcome of the data models. This prediction finds its utility in almost all areas from sports, to TV ratings, corporate earnings, and technological advances. Predictive modeling is also called predictive analytics. With the help of predictive analytics, we can connect data to effective action about the current conditions and future events. Also, we can enable the business to exploit patterns and which are found in historical data to identify potential risks and opportunities before they occur.
Python is used for predictive modeling because Python-based frameworks give us results faster and also help in the planning of the next steps based on the results.
Our course at EDUCBA is tailor-made for people who are willing to work with a framework that delivers the best result in comparison to the rest of the competitive tools in the market.
Which Skills will you learn in this Training?
Our course ensures that you will be able to think with a predictive mindset and understand well the basics of the techniques used in prediction. Critical thinking is very important to validate models and interpret the results. Hence, our course material emphasizes on hardwiring this similar kind of thinking ability.
You will have good knowledge about the predictive modeling in python, linear regression, logistic regression, the fitting model with a sci-kit learn library, the fitting model with stat model library, ROC curves, backward elimination approach, stats model package, etc.
Pre-requisites
- To get started with Predictive Modelling with Python a solid foundation in statistics is much appreciated. It takes a good amount of understanding to interpret those numbers to understand whether the numbers are adding up or not.
- Along with the above-mentioned knowledge, one must know to code in Python.
- Knowing SQL also acts as a complementary skillset.
- Even if someone is not well equipped with the above-mentioned skill, it should not act as a hindrance as everything is possible with an honest effort and strong will.
Target Audience
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- This Predictive Modeling with Python Course can be taken up by anyone who shares a decent amount of interest in this field. The earlier someone starts the further they can reach. In the case of students who are pursuing a course in statistics, or computer science graduates it is a very good opportunity to direct your career in that direction. As this is a much demand skill every IT professional is looking for a good switch and entering the domain of predictive analysis.
- After successfully having hands-on with Predictive Analysis you get open up career opportunities within job roles like that of a Data Analyst, Data Scientist, Business Analyst, Market Research Analyst, Quality Engineer, Solution Architect, Programmer Analyst, Statistical Analyst, Statistician, etc.
Predictive Modeling with Python Course – FAQ’s
How is predictive modeling different from that of forecasting?
In Predictive modeling, we use data mining and probability to forecast the outcomes. There are several predictors which are variables that influence future results. Once the data is fetched for relevant predictors, a statistical model is formulated.
Do I receive a certificate at the end of completing this Predictive Modeling with the Python Course?
Yes, a certificate is handed out on completing the online training bundle. You can issue for the certificate once you have completed 70% of the course content for this particular course.
I am a working professional, is this Predictive Modeling with Python Course for me?
This is a self-paced course and this can be taken up by anyone who has interests in this subject and can be completed even from the comfort of your home.
Sample Preview
Career Benefits
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- Predictive modeling is a field which has immense growth in line in due years to come due to the definite explosion of data that we are noticing. In the year 2017, it was forecasted by IBM that the demand for data scientists and analytical professionals will grow by 15% in the year 2020.
Many companies have realized the importance of using predictive modeling for their business but currently, there is a shortage of skilled professionals. A substantial amount of salaries is offered to people with this skillset because of the nature of the job.
The demand for qualified candidates is increasing at a significant rate.
It is the right time to invest in learning for such a niche skill as the market for predictive analytics is not coming down any sooner. EDUCBA is the right platform for getting you to achieve your goals as we understand the need of the industry and update our course and course content accordingly.
Reviews
Great course
Great video learning! It is taught nice and clear. At first, a bit slow, but as the course progressed, was the tempo at just the right place with good articulation. The content was good, with some nice examples worked out and examples from real life, but could be made more elaborate. Looking forward to more courses of the same teacher.
Linked
Nyckees Daan
Predictive Modeling
This is a good course for those who have zero or little knowledge of predictive modeling. It covers most of the algorithms to do predictive modeling. It also provides some examples and sample questions for practice. I wish this could provide more study material and more practical questions.
Linked
Lee Tze Hui
Glad that I enrolled
This is a good course to learn the basics of financial analysis using statistical tools in excel. Overall this course is quite good enough to get basic ideas on different statistical tools like- mean, geometric mean, standard deviation, and many more. I’m quite happy that I got the chance to enroll in this course.
Linked
Rakibul Hossein
