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What you'll get
- 19+ Hours
- 6 Courses
- Mock Tests
- Course Completion Certificates
- One year access
- Self-paced Courses
- Technical Support
- Mobile App Access
- Case Studies
Synopsis
- Learn to program in Python at a good level
- Learn how to code in Jupiter Notebooks
- Learn the core principles of programming
- Learn how to create variables
- Learn about integer, float, logical, string and other types in Python
- Learn how to create a while() loop and a for() loop in Python
- Learn how to install packages in Python
- Understand the Law of Large Numbers
Content
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MODULE 1: Essentials Training
Courses No. of Hours Certificates Details Data Science with Python 3h 17m ✔ Statistics Essentials with Python 3h 23m ✔ Advanced Python for Data analysis 6h 29m ✔ Test - Data Science with Python Test Series -
MODULE 2: Projects based Learning
Courses No. of Hours Certificates Details Logistic Regression & Supervised Machine Learning in Python 2h 6m ✔ Sales Forecasting using Time Series Analysis in Python 2h 13m ✔ Linear Regression & Supervised Learning in Python 2h 28m ✔ -
MODULE 3: Mock Exams & Quizzes
Courses No. of Hours Certificates Details Test - Data Science with Python Major 1 Test - Data Science with Python Major 2
Description
About Python for Data Science with Real Exercises
There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different. This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward. After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises. In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course. Apply Data Science using Python, Statistical Techniques, EDA, Numpy, Pandas, Scikit Learn, Statsmodel Libraries.
Are you aspiring to become a Data Scientist or Machine Learning Engineer? if yes, then this course is for you. In this course, you will learn about core concepts of Data Science, Exploratory Data Analysis, Statistical Methods, role of Data, Python Language, challenges of Bias, Variance and Overfitting, choosing the right Performance Metrics, Model Evaluation Techniques, Model Optmization using Hyperparameter Tuning and Grid Search Cross Validation techniques, etc. You will learn how to perform detailed Data Analysis using Pythin, Statistical Techniques, Exploratory Data Analysis, using various Predictive Modelling Techniques such as a range of Classification Algorithms, Regression Models and Clustering Models. You will learn the scenarios and use cases of deploying Predictive models. This course covers Python for Data Science and Machine Learning in great detail and is absolutely essential for the beginner in Python. Most of this course is hands-on, through completely worked out projects and examples taking you through the Exploratory Data Analysis, Model development, Model Optimization and Model Evaluation techniques. This course covers the use of Numpy and Pandas Libraries extensively for teaching Exploratory Data Analysis. In addition, it also covers Marplotlib and Seaborn Libraries for creating Visualizations.
Requirements
- Be Able To Use PC At A Beginner Level, Including Being Able To Install Programs
- A Desire To Learn Data Science
- Prior Knowledge Of Python Will Be Useful But NOT Necessary
Target Audience
- This course if for you if you want to learn how to program in Python
- This course is for you if you are tired of Python courses that are too complicated
- This course is for you if you want to learn Python by doing
- This course is for you if you like exciting challenges
- You WILL have homework in this course so you have to be prepared to work on it
Course Ratings
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I found this course very helpful. The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills
JIYEON CHOI
Very Informative and Well Organized Course Contents. High Quality Videos. I will recommend this course to anyone I know who interested to learn about Data Analytics.
JOSEPH WONG
I recently completed a data analytics course and found it to be an incredibly valuable learning experience. The course provided a comprehensive introduction to data analytics, covering everything from data collection and cleaning to advanced statistical analysis and data visualization. One thing I appreciated about the course was the hands-on approach to learning. Throughout the course, we worked with real datasets and used industry-standard tools such as Python, R, and Tableau to analyze and visualize the data. This gave me the practical skills and experience I needed to feel confident in my ability to work with data in a professional setting. The course instructors were knowledgeable and engaging, and they were always available to answer questions and provide feedback. The course also had a supportive and active online community, where I was able to connect with other learners and share my experiences and insights. Overall, I would highly recommend this data analytics course to an
Akram Ahmed
The Data Science Fundamentals online course that I recently completed. Overall, I found the course to be highly valuable and informative. The content was well-structured and provided a solid foundation for understanding key concepts in data science.
Priti Gajanan Patole