Become a Python Data Analyst
Access Duration - 365 Days
Accredited By "CPD & iAP"
Guided Learning Hours (05)
Course Material
Number of Modules 06
Mock Exam Included
Final Exam Included
4.5( 4 REVIEWS )
142 STUDENTS
Accredited By
Overview
Get to learn all what it takes to be a skilled Python data analyst, along with insights into real-world examples, by taking this wonderful course in Become a Python Data Analyst. This course has been developed to guide individuals with a basic knowledge on Python to enhance their data analysis skills using the Python libraries and tools, and could be a great way to boost up their candidature to get into or progress within the data analysis, data science, business analysis, business intelligence and web development fields of work.
This e-course will start off by walking you through Anaconda distribution that allows you to access all the popular Python libraries, followed up by a tour on the Jupyter notebook computing platform that is essential for basic python coding purposes. This course will then move you onto explore the NumPy library of the Python programming language that is widely used in numerical and scientific computing activities and the Panda library that allows data analysis using Pytho.
You will get to discover the concepts essential to use Mtplotlib in Python and the Exploratory Data Analysis to apply them in real-world dataset. The SciPy library in Python that is used in statistical computing will also be discussed in-depth through this course, along with attention to the concepts of predictive analysis models and its relationship with machine learning. By the end of this course, you will have a well-grounded knowledge on data analysis using Python to make significant progress within the industry.Â
Why study at Global Edulink?
Global Edulink offers the most convenient path to gain recognised skills and training that will give you the opportunity to put into practice your knowledge and expertise in an IT or corporate environment. You can study at your own pace at Global Edulink and you will be provided with all the necessary material, tutorials, qualified course instructor, narrated e-learning modules and free resources which include Free CV writing pack, free career support and course demo to make your learning experience more enriching and rewarding.
- Access DurationÂ
- Who is this course aimed at?
- Entry Requirements
- Method Of Assessment
- Certification
- Career Path
- Other benefits
The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lectures at any time.
This course might interest individuals aspiring to or already in the fields of data analysis, data science, business intelligence, business analysis and web development
- Learners must be age 16 or over and should have basic understanding of the English Language, numeracy, literacy and ICT.
- A basic knowledge or experience in Python programming is required
The course is assessed online with a final, multiple-choice test, which is marked automatically. You will know instantly whether you have passed the course.
Those who pass this test will get a certificate in Become a Python Data Analyst
With this certificate in your resume, you are sure to impress potential employers into hiring you for a number of jobs in the job market. This certificate will also pave way for your career progression by putting you in line to demand for salary increments of job promotions from your employer. Mentioned below are some of the jobs this certificate will help you land, along with the average UK salary per annum according to https://www.payscale.com/career-path-planner,
- Data analyst – £25,972 per annum
- Data scientist – £35,226 per annum
- Data Manager – £29,986 per annum
- Software engineer – £35,190 per annum
- Software developer - £30,753 per annum
- Business Intelligence (BI) developer – £35,618 per annum
- Business Intelligence (BI) analyst – £30,483 per annum
- High-quality e-learning study materials and mock exams.
- Tutorials/materials from the industry leading experts.
- 24/7 Access to the Learning Portal.
- The benefit of applying for TOTUM extra Discount Card.
- Recognised Accredited Qualification.
- Excellent customer service and administrative support
Course Curriculum
| Additional Materials & Code Files | |||
| Become a Python Data Analyst – Code Files | |||
| Section 1: The Anaconda Distribution and the Jupyter Notebook | |||
| 1.1. The Course Overview | 00:00:00 | ||
| 1.2. The Anaconda Distribution | 00:00:00 | ||
| 1.3. Introduction to the Jupyter Notebook | 00:00:00 | ||
| 1.4. Using the Jupyter Notebook | 00:00:00 | ||
| Section 2: Vectorizing Operations With NumPy | |||
| 2.1. NumPy: Python’s Vectorization Solution | 00:00:00 | ||
| 2.2. NumPy Arrays: Creation, Methods and Attributes | 00:00:00 | ||
| 2.3. Using NumPy for Simulations | 00:00:00 | ||
| Section 3: Pandas: Everyone’s Favorite Data Analysis Library | |||
| 3.1. The Pandas Library | 00:00:00 | ||
| 3.2. Main Properties, Operations and Manipulations | 00:00:00 | ||
| 3.3. Answering Simple Questions about a Dataset – Part 1 | 00:00:00 | ||
| 3.4. Answering Simple Questions about a Dataset – Part 2 | 00:00:00 | ||
| Section 4: Visualization and Exploratory Data Analysis | |||
| 4.1. Basics of Matplotlib | 00:00:00 | ||
| 4.2. Pyplot | 00:00:00 | ||
| 4.3. The Object Oriented Interface | 00:00:00 | ||
| 4.4. Common Customizations | 00:00:00 | ||
| 4.5. EDA with Seaborn and Pandas | 00:00:00 | ||
| 4.6. Analysing Variables Individually | 00:00:00 | ||
| 4.7. Relationships between Variables | 00:00:00 | ||
| Section 5: Statistical Computing With Python | |||
| 5.1. SciPy and the Statistics Sub-Package | 00:00:00 | ||
| 5.2. Alcohol Consumption – Confidence Intervals and Probability Calculations | 00:00:00 | ||
| 5.3. Hypothesis Testing – Does Alcohol Consumption Affect Academic Performance? | 00:00:00 | ||
| 5.4. Hypothesis Testing – Do Male Teenagers Drink More Than Females? | 00:00:00 | ||
| Section 6: Introduction to Predictive Analytics Models | |||
| 6.1. Introduction to Predictive Analytics Models | 00:00:00 | ||
| 6.2. The Scikit-Learn Library – Building a Simple Predictive Model | 00:00:00 | ||
| 6.3. Classification – Predicting the Drinking Habits of Teenagers | 00:00:00 | ||
| 6.4. Regression – Predicting House Prices | 00:00:00 | ||
| Mock Exam | |||
| Mock Exam : Become a Python Data Analyst | 00:40:00 | ||
| Exam | |||
| Exam : Become a Python Data Analyst | 00:40:00 | ||
| Certificate Download Guide | |||
| Certificate Download Guide | 00:03:00 | ||
Nusurath Kaitheri Chalil
Fantastic course
Lukas Davies
Intuitive and practical course. And the tutor was well organised.
Dottie Wilson
I was searching for a Python course that teaches predictive modelling and this course was a good fit. Also, the approach taken to use a case study to teach the concept was very good. If links were provided it would certainly be more helpful.
Aaron Willis
If you want to learn python programming, this is a good place to start. A good course that contains the right amount of information.