Learn from Home Offer
Data Science with Python Training (21 Courses, 12+ Projects)
This Data Science with Python Course includes 21 courses, 12 Projects with 89+ hours of video tutorials and Lifetime access.
You will also get verifiable certificates (unique certification number and your unique URL) when you complete each of them. This training is for you to learn Python programming, statistics, machine learning algorithms and its application along with data visualization.
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1
Machine Learning with SciKit-Learn in Python
2
Projects on ML - Predictive Modeling with Python
3
Matplotlib for Python Developers (3 Courses)
4
Pandas with Python Tutorial
5
NumPy and Pandas
Offer ends in:
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* One Time Payment & Get Lifetime Access
* One Time Payment & Get Lifetime Access
What you get in this Data Science with Python Training?
About Data Science with Python Course
Lets now discuss the details about the Data Science with Python Course in detail.
| Course | No. of Hours | |
|---|---|---|
| Machine Learning with SciKit-Learn in Python | 8h 46m | |
| Projects on ML - Predictive Modeling with Python | 9h 44m | |
| Machine Learning using Python | 3h 13m | |
| Matplotlib for Python Developers - Beginners | 4h 24m | |
| Matplotlib for Python Developers - Intermediate | 2h 6m | |
| Matplotlib for Python Developers - Advanced | 6h 5m | |
| Pandas with Python Tutorial | 6h 05m | |
| NumPy and Pandas | 5h 07m | |
| Project on Pandas - Data Management for Retail Dataset | 4h 18m | |
| Project on Python - Sentiment Analysis | 1h 08m | |
| Seaborn - Beginners | 2h 34m | |
| Seaborn - Intermediate | 1h 22m | |
| Seaborn - Advanced | 1h 6m | |
| PySpark - Beginners | 2h 33m | |
| PySpark - Intermediate | 2h 16m | |
| PySpark - Advanced | 1h 21m | |
| Data Science with Python | 4h 22m | |
| Artificial Intelligence with Python | 6h 36m | |
| OpenCV for Beginners | 2h 28m | |
| Video Analytics Using Opencv and Python Shells | 2h 21m | |
| Statistics for Data Science using Python | 3h 56m | |
| Project on Tensorflow - Implementing Linear Model with Python | 1h 49m | |
| Project - Data Analytics with Data Exploration Case Study | 5h 19m | |
| Project on ML - Random Forest Algorithm | 1h 31m | |
| Python for Finance | 2h 12m | |
| Financial Analytics with Python | 2h 11m | |
| Project - Linear Regression in Python | 2h 15m | |
| Project - House Price Prediction using Linear Regression | 2h 8m | |
| Project on Python Data Science - Predicting the Survival of Passenger in Titanic | 2h 11m | |
| Project - Credit Default using Logistic Regression | 3h 9m | |
| Forecasting the Sales using Time Series Analysis in Python | 2h 29m | |
| Machine Learning with Python Project - Predict Diabetes on Diagnostic Measures | 1h 07m | |
| Project on Machine Learning - Develop Movie Recommendation Engine with Python | 53m |
| Course Name | Online Data Science with Python Course Bundle |
| Deal | You get access to all 21 courses, 12 Projects bundle. You do not need to purchase each course separately. |
| Hours | 89+ Video Hours |
| Core Coverage | Data Science with Python, Artificial Intelligence with Python, Video Analytics Using OpenCV and Python Shells, Pandas with Python Tutorial, Machine Learning using Python, Statistics for Data Science using Python |
| Course Validity | Lifetime Access |
| Eligibility | Anyone serious about learning Data science using Python and want to make a career in Data and analytics |
| Pre-Requisites | Basic knowledge of Data Science and Python programming |
| What do you get? | Certificate of Completion for each of the 21 courses, 12 Projects |
| Certification Type | Course Completion Certificates |
| Verifiable Certificates? | Yes, you get verifiable certificates for each course 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 |
| Software Required | Open-Source Python |
| System Requirement | PC or laptop with processor i3+ and RAM recommendation should be at least 4GB with smooth internet connectivity |
| Other Requirement | Speaker / Headphone |
Online Data Science with Python Course Curriculum
In this section, each module of the Data Science with Python Course is explained briefly:
Goals
The main goal of the course is to provide a deeper understanding and hands-on learning experience on the Data Science domain with the help of Python programming language along with real-time Data Science projects to provide an overall knowledge on Data Science domain.
Objectives
This course offers a deep and wide range of skills set from Programming to statistics and machine learning algorithms. The skills you will attain from this course could make you an expert Data Analyst, Quality Analyst and Business Analyst and Statistical Analyst roles.
Machine learning algorithms such as Regression, Clustering, Classification and prominent libraries such as Pandas, Matplotlib, SciKit -learn is covered from this course.
Course Highlights
This course covers all the topics from Mathematics to Programming to Visualization techniques that are needed for a Data Scientist role. The whole module that is provided is based on recent trends and growing job opportunities in the Data Science world.
- The course provides the module of Machine Learning with a prominent SciKit -Learn library. Algorithms like Regression, Clustering, and Classification are done using the SciKit-Learn library.
- Pandas library which is used prominently for Data Analysis, Data wrangling and analytics is covered extensively in this course.
- Data Visualization library Matplotlib is covered from Beginner to Advance level in this course it is very useful for pictorial representation and Data Reporting.
- PySpark library which is useful for processing different kinds of structured and unstructured datasets and also can be used in getting API for working on different format data.
- Seaborn library which can also be used for Data Visualization is covered from beginner to advanced level.
- AI and Statistics module using python is also covered in this course which is very useful in gaining core data science skills.
- Financial analytics using python is covered to provide a much needed and valuable knowledge on how Data Science is used in the financial platform.
- Video Analytics using Opencv (computer vision) library which helps analyze video format data is covered in this course which would be very helpful in gaining insights form Video data.
Project Highlights
- A project like Diabetes prediction is covered in this course with a real-time dataset to classify the patients who have the risk of getting diabetes from their historical medical data.
- Deep learning library TensorFlow is covered in this course for a project to build a Regression model.
- Complete Data Analytics and Exploratory analysis are covered as a project in this course. This project will provide the skill to examine and study a dataset to gain complete information before building any models.
- Project on Machine Learning algorithm called Random Forest which is a very widely used classification technique to get accurate results is also covered in this course.
- Projects on Linear regression which are used to predict the continuous value in a dataset are covered in this course. Linear Regression is a very popular and widely used algorithm to predict the continuous value in real-time data. Sales prediction is done using Linear regression.
- A very common ML project with a Titanic Survivor prediction using the Titanic dataset is worked out to gain a practical understanding of classification algorithms.
- A very important project that has been very popular in recent times known as Recommendation Systems which used in almost all the user interactive platforms from YouTube to Netflix is covered in this course. The recommendation system decides the sales of a product by making the customers choose from their likeness by studying their historical behavior and viewing patterns.
- A very important financial domain project that is very widely used in the banking sector known as credit card defaulter prediction and also the prediction of Housing price from a whole range of features of a house is practically worked out in this course.
On the whole, the course module provides Hands-on training and project experiences that will help you gain knowledge on the complete skill set that is required in the Data Science domain.
Industry Growth Trend
 The overall data science platform market is expected to grow from USD 19.58 billion in 2016 to USD 101.37 billion by 2021, at a CAGR of 38.9% from 2016 to 2021.[Source - MarketsandMarkets]
Average Salary
Average Salary$120,301 per year
The average salary for a Data Scientist is $120,301 per year in the United States.
[Source - Indeed]
[Source - Indeed]
Certificate of Completion
Pre-requisites
- Basic Knowledge of Computer Programming terminologies:Â Any candidate looking for this Data Science with Python Course should be familiar with any of the programming languages. It will add weight if you know the basics of statistics. We design our course starting with the basics. We will make sure to deliver you with all the fundamental knowledge.
- System Configuration:Â We recommend to use any PC or laptop with processor i3+ and RAM recommendation should be at least 4GB with smooth internet connectivity.
- Passion to Learn:Â You need to start your course with high motivation and passion to learn. Be honest with yourself.
Target Audience
- Professional looking for Analytics:Â Every sector of the industry is moving towards analytics. They are working with a high volume of data for their business growth. They are attracting new tools and technologies.
- Job career:Â People who are keen to learn and work in domains like Machine Learning, Artificial Intelligence, Business Intelligence, Data Analyst.
- Any Individuals: Data Engineers, Software Developer, Analyst, IT operations, Architects, Technical Managers, Professional interested in Data Science and Visualisations.
FAQ’s- General Questions
What are the system requirements?
We highly recommend to use a system as per below  minimum specifications:
- Processor: i3 +
- RAM: 4 GB
- Hard Disk – 128 GB
Can I access this data science with a Python course from anywhere?
Yes, you can access this Data Science with Python Course from anywhere & everywhere. You can connect to us through your laptop and tablet with a fair quality of internet speed. After registration, you will get a unique user Id and password to access this course.
When will I receive my certificate for the Python Data Scientist course?
We will issue a certificate once you are done with 70% of the training materials provided for this bundle course.
Will I get any technical Support during my Training at eduCBA?
Yes, we will provide you with technical support for this course. We will connect you with a highly skilled trainer to get your doubt cleared.
How much this Data Science with Python Course will help me to get a job?
We have designed our courser as per industry requirements. Our rich content will help you to learn different tools and applications in the industry. We will help you with projects and case studies which will help you to develop your profile.
Do you provide placement assistance after completing this Data Science with Python Course?
No, this is just a skill graduation course.
Course Testimonials
Data science
The Data Science with Python course goes through the different areas of data science with python. Besides a fundamental theory regarding the explained concepts, the diverse concepts are exemplified with short python programs. The lessons are good to understand and the programs presented to illustrate and implement the concepts are simple and significant.
Linked
Jorge Giro
Artificial Intelligence
This video training course was created in a very structured way and easy to understand. The fundamentals and concepts of Artificial Intelligence were well explained with simplicity approach. The demonstration of using Python and iPython provides an overview of how the application works internally.
CHONG FONG KONG
Perfect Pandas Primer
I enjoyed this Data Science with the Python course. It seemed to be very up-to-date. The instructors were clear, concise, and thorough. The structure was intuitive and presented understandably, building on each step and using data frames and sets that avoided confusion by using names that didn’t sound like operational commands and vice versa. Grammar and logic were exceptional, with very little wasted time. A great introduction to using Pandas.
Linked
Michael Williams
