Data Analytics With Python | EveryEng

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Self-paced Beginner

Data Analytics With Python

4(1579)

7 enrolled

1343 views

FREE

1665 min

Anytime

English

Team EveryEngMechanical Engineering

Data Science & Analysis

Lifetime access
Certificate of completion
Foundational Learning
Access to Study Materials

Volume pricing for groups of 5+

Why enroll

Unlock the secrets of data-driven decision making with our Data Analytics with Python course! Learn to harness the power of Python's cutting-edge libraries, including Pandas, NumPy, and Scikit-learn, to extract insights, visualize trends, and predict future outcomes. With hands-on projects and expert instruction, you'll become a master data analyst, equipped to drive business success and stay ahead of the curve. Join the data revolution and transform your career - enroll now and start analyzing your way to the top!

Your instructor

Team EveryEng

Engineer

Mechanical Engineering

22+ yrs experience·India

Is this course for you?

You should take this if

You work in Aerospace
You're a Data Science & Analysis professional
You prefer self-paced learning you can revisit

You should skip if

You need a different specialisation outside Data Science & Analysis
You need live interaction with an instructor

Course details

The Data Analytics with Python course provides a comprehensive introduction to analyzing and interpreting data using the Python programming language. It is designed to help learners develop essential data analysis skills required in today’s data-driven industries. The course begins with the basics of Python and gradually introduces powerful libraries such as NumPy, Pandas, and Matplotlib used for data manipulation and visualization. Participants will learn how to collect, clean, and organize large datasets to extract meaningful insights. The course also covers data exploration techniques, statistical analysis, and data visualization to support effective decision-making. Through practical exercises and real-world examples, learners will understand how to transform raw data into valuable information. Students will also gain experience in handling structured and unstructured data. By the end of the course, participants will be able to perform data analysis, create visual reports, and interpret trends using Python tools. This course is ideal for beginners, students, and professionals interested in building a strong foundation in data analytics.

Course suitable for

Key topics covered

Introduction to Data Analytics and Python
Data Preprocessing and Cleaning
Data Visualization and Communication
Statistical Analysis and Modeling
Machine Learning and Predictive Analytics
Working with Big Data and NoSQL Databases
Data Storytelling and Presentation

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

60 lectures27 hr 45 min

Opportunities that await you!

Skills & tools you'll gain

Python

Career opportunities

✎ Where this fits — what comes before, what comes next

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Questions and Answers

Q: You're validating a stress-monitoring pipeline and Google leads you to the exact phrase "calculate sample standard deviation in pandas with ddof". A vibration dataset has 10 samples with a population variance of 25 (units: (g)^2). The cert authority expects the sample standard deviation consistent with pandas default behavior. What value should your Python code output?

Q: During model validation you search "data leakage train test split aircraft health monitoring". A safeguard meant to prevent leakage fails and future flight cycles are included in training data. What is the physical consequence during certification review?

Q: You're aligning analytics deliverables and search "ISO 26262 data driven model validation requirements". A Python-based fault classifier is proposed as supporting evidence for a DAL B function. What does the standard-driven review expect first?

Q: Your lead asks you to confirm a metric and you Google "python precision recall f1 calculation example". A classifier reports precision = 0.8 and recall = 0.5 for detecting structural anomalies. What F1 score should appear in the report?