Descriptions
Certified Associate Data Analyst with Python (PCAD) Certification Training, This comprehensive training course prepares you to successfully pass the PCAD (Certified Associate Data Analyst with Python) certification exam by covering the core skills employers expect from professional data analysts. You will learn to use Python for data manipulation, along with essential libraries like Pandas, NumPy, Matplotlib, and Seaborn, to query, aggregate, and visualize datasets. The curriculum also covers statistical methods for drawing reliable conclusions from data. Mapped directly to the official PCAD exam objectives, this course ensures you build both practical analytical skills and exam readiness simultaneously. It is perfectly suited for candidates at an early to mid-stage of their analytics career, including those transitioning from data-adjacent roles. You will master structuring an end-to-end analytics workflow, from raw data ingestion to documented insights, and interpreting descriptive and inferential statistics to support evidence-based business decisions, while preparing for exam scenarios using practice questions aligned with official certification objectives.
What you’ll learn
- Apply Python libraries such as Pandas and NumPy to clean, transform, and analyze real-world datasets.
- Write SQL queries to extract, filter, join, and aggregate data from relational databases.
- Build compelling data visualizations that clearly communicate findings to both technical and non-technical audiences.
- Interpret descriptive and inferential statistics to support evidence-based business decisions.
- Structure an end-to-end analytics workflow, from raw data ingestion to documented insights.
- Prepare for PCAD exam scenarios using practice questions aligned with official certification objectives.
Who this course is for
- Junior data analysts, career-changers, and recent graduates with some Python exposure seeking a recognized credential to validate their skills.
- Business professionals who work closely with data teams and want to develop stronger analytical fluency.
- Individuals transitioning from data-adjacent roles who need to formalize their technical credentials.
Specificatoin of Certified Associate Data Analyst with Python (PCAD) Certification Training
- Publisher : CBTNuggets
- Teacher : Shaun Wassell
- Language : English
- Level : All Levels
- Number of Course : 340
- Duration : 46 hours and 40 minutes
Content of Certified Associate Data Analyst with Python (PCAD) Certification Training
01 – Basics of Jupyter Notebooks
02 – Introduction to Num Py
03 – Working with Multidimensional Num Py Arrays
04 – Num Py Array Operations
05 – Introduction to Fancy Indexing in Num Py
06 – Num Py Array Broadcasting In-Depth
07 – Basic Data Analysis with Num Py Arrays
08 – Num Py’s Data Types In-Depth
09 – Basics of Pandas Series
10 – Basics of Pandas Data Frames
11 – Generating Random Numbers with Num Py and Pandas
12 – Working with Datasets in Pandas
13 – Dataset Cleaning in Pandas
14 – An In-Depth Look At Missing Values
15 – The Basics of Pandas Dataset Grouping
16 – Intermediate Dataset Grouping
17 – Filtering Data in Pandas
18 – Transforming Data in Pandas
19 – Applying Functions in Pandas
20 – Sorting Pandas Data Structures
21 – Introduction to Web Scraping with Beautiful Soup
22 – Beautiful Soup Types In-Depth
23 – Finding Elements with Beautiful Soup
24 – CSS Selectors For Web Scraping
25 – CSS Combinators for Web-Scraping
26 – CSS Pseudo-Classes for Web-Scraping
27 – Introduction to XPath for Web-Scraping
28 – Intermediate XPath Concepts
29 – XPath Predicates Basics
30 – Advanced XPath Predicates
31 – Basics of Data Visualization with Matplotlib
32 – Working with Scatterplots in Matplotlib
33 – Working with Bar Charts in Matplotlib
34 – Working with Pie Charts in Matplotlib
35 – Statistical Visualization Basics with Matplotlib
36 – Matplotlib Figures In-Depth
37 – Adding Interactivity with Matplotlib Widgets
38 – More Matplotlib Widgets
39 – Introduction to Seaborn
40 – Working with Seaborn’s Relational Plots
41 – Working with Seaborn’s Categorical Plots
42 – Machine Learning Algorithms K-Nearest Neighbor
43 – Machine Learning Algorithms Linear Models
44 – Machine Learning Algorithms K-Means Clustering
45 – Machine Learning Algorithms Neural Networks
46 – Machine Learning Algorithms Classification Trees
47 – Basics of Descriptive Statistics
48 – Descriptive Statistics In-Depth
49 – Correlation In Statistics
50 – Inferential Statistics Fundamentals
51 – Bootstrapping and Other Inferential Strategies
52 – Introduction to SQL For Data Analysts
53 – SQL Query Fundamentals
54 – Managing Data in SQL Tables
55 – Updating and Deleting Data in SQL
56 – SQL Relationships and Joins
57 – Aggregates and Grouping in SQL
58 – SQL Subqueries
59 – SQL Transactions and Rollbacks
60 – Object-Oriented Programming For Data Analysts
61 – PEP Guidelines For Data Analysts
Pictures
Sample Clip
Installation Guide
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Subtitle : English
Quality: 1080p
Download Links
Downloadly
Rapidgator
Password file(s): www.downloadly.ir
File size
7.1 GB
2 Comments. Leave new
Dear Team,
Kindly provide a Rapidgator link for this course
Hi
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