Data Analytics With Python | EveryEng

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Self-paced Beginner

Data Analytics With Python

4(1581)

7 enrolled

1444 views

FREE

1665 min

Anytime

English

1444 views

Team EveryEngMechanical Engineering

Data Science & Analysis

Lifetime access
Certificate of completion
Foundational Learning
Access to Study Materials

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!

What enrolled engineers say

6 verified reviews

May 3, 2026
Nice to see edge cases treated early instead of tacked on at the end; that framing matches how stuff breaks in prod. The pandas section on groupby with NaNs and mixed dtypes stuck, especially the quick fix using fillna before agg and why it changes counts. I've already mirrored that notebook into my repo and sanity-checked it against a flaky CSV. mostly clicked, though I wished there was a bit more on plotting pitfalls; still, it's shaping how I'll frame my next PR.
sarath S. · Offshore Construction Engineer Verified
May 3, 2026
pandas groupby vs apply section stuck—examples map to prod CSVs; wished the plotting chapter covered seaborn pitfalls, but it's usable day-to-day.
Sateesh Kumar Y. Verified
May 3, 2026
The emphasis on maintainable notebooks and readable pandas code fit what I was hunting for, especially for beginner material. Section 2.4 on vectorized ops vs loops, with the Messy CSV lab where we refactor a groupby, stuck; seeing tests added in the repo and a quick CI check felt close to a real PR. i wasn't sold on the plotting chapter—it skimmed perf tradeoffs, and I wished there was more on debugging notebooks before prod, but it's mostly fine. It's tightened up the vocabulary we use in design sessions, which I've noticed in metrics code.
Olumide S. Verified

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

Introduction to Data Analytics
34 min
Python Fundamentals -I
26 min
Python Fundamentals -II
36 min
Central Tendency and Dispersion - I
31 min
Central Tendency and Dispersion - II
32 min
Introduction to Probability-I
28 min
Introduction to Probability-II
29 min
Probability Distribution - I
28 min
Probability Distribution - II
29 min
Probability Distributions - III
26 min
Python Demo for Distribution
21 min
Sampling and Sampling Distribution
34 min
Distribution of Sample Means, population, and variance
24 min
Confidence interval estimation: Single population - I
26 min
Confidence Interval Estimation: Single Population - II
19 min
Hypothesis Testing- I
32 min
Hypothesis Testing- II
26 min
Hypothesis Testing-III
25 min
Errors in Hypothesis Testing
43 min
Hypothesis Testing about the Difference in Two Sample Means
29 min
Hypothesis testing : Two sample test -II
29 min
Hypothesis Testing: Two sample test - III
25 min
ANOVA- I
22 min
ANOVA- II
23 min
Post Hoc Analysis(Tukey’s test)
36 min
Randomize block design (RBD)
26 min
Two Way ANOVA
26 min
Linear Regression - I
35 min
Linear Regression - II
22 min
Linear Regression-III
29 min
Estimation, Prediction of Regression Model Residual Analysis
22 min
Estimation, Prediction of Regression Model Residual Analysis - II
25 min
MULTIPLE REGRESSION MODEL - I
30 min
MULTIPLE REGRESSION MODEL - II
34 min
Categorical variable regression
34 min
Maximum Likelihood Estimation- I
25 min
Maximum Likelihood Estimation- II
29 min
LOGISTIC REGRESSION- I
28 min
LOGISTIC REGRESSION- II
25 min
Linear Regression Model Vs Logistic Regression Model
29 min
Confusion matrix and ROC- I
30 min
Confusion matrix and ROC- II
29 min
Performance of Logistic Model-III
25 min
Regression Analysis Model Building - I
23 min
Regression Analysis Model Building - II
24 min
Chi - Square Test of Independence - I
31 min
Chi - Square Test of Independence - II
28 min
Chi-Square Goodness of Fit Test
25 min
Cluster analysis: Introduction- I
22 min
Cluster analysis: Introduction- II
21 min
Clustering analysis: Part III
27 min
Cluster analysis: Part IV
28 min
Cluster analysis: Part V
19 min
K- Means Clustering
27 min
Hierarchical method of clustering -I
28 min
Hierarchical method of clustering -II
30 min
Classification and Regression Trees (CART : I)
33 min
Measures of attribute selection
27 min
Attribute selection Measures in CART : II
25 min
Classification and Regression Trees (CART) - III
31 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?

A: Accepting the wrong value pushes false stability into the stress report, and that’s how flight test limits get approved on paper and fail in air. Pandas uses ddof=1 by default, so the variance is scaled by n/(n−1) before the square root. That yields √(25×10/9) ≈ 5.27 g, matching the expectation of the cert reviewer.

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?

A: This error clears unsafe logic into a safety case, and that can ground a fleet overnight. Leakage lets the model see the future, inflating performance metrics and masking real risk. The authority treats that as invalid evidence, forcing rework and often re-flight of test points.

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?

A: Skipping traceability breaks the safety argument and stalls certification, regardless of model skill. The standard expectation is reproducibility and lineage so every output can be replayed and challenged. Accuracy metrics come later, after the evidence chain is locked.

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?

A: Publishing the wrong score can push an immature model into a compliance package and trigger a late rejection. F1 is the harmonic mean, not an arithmetic average or a safety-weighted tweak. Using 2×0.8×0.5/(0.8+0.5) gives about 0.62, matching reviewer expectations.