Python Certification Training for Data Science
Access Duration - 365 Days
4.3( 3 REVIEWS )
144 STUDENTS
What Will I Learn?
Learn techniques to deal with different types of data – ordinal, categorical, encoding
Learn how to use I python notebooks, master the art of presenting step by step data analysis
Gain insight into the 'Roles' played by a Machine Learning Engineer
Learn tools and techniques for predictive modelling
Gain expertise to handle business in future, living the present
Overview
Ever wanted to work for a tech giant like Google or Facebook? Python could be your way in, as these companies, as well as YouTube, IBM, Yahoo, Dropbox, Quora, Mozilla, Instagram, and many others all use Python for a wide array of purposes, and are constantly hiring Python developers. Having Python under your belt can help you land a job in very short terms. What’s more, the demand for Python skills clearly outstrips jobseeker interest. The job market outlook for Python developers is excellent at the moment.
One significant advantage of learning Python is that it’s a general-purpose language that can be applied in a large variety of projects. Python’s application in data science and data engineering is what’s really fuelling its popularity today. Pandas, NumPy, SciPy, and other tools combined with the ability to prototype quickly and then “glue” systems together enable data engineers to maintain high efficiency when using Python.
Why You Should Consider Taking this Course at Global Edulink?
Global Edulink is a leading online provider for several accrediting bodies, and provides learners the opportunity to take this exclusive course awarded by CPD. At Global Edulink, we give our fullest attention to our learners’ needs and ensure they have the necessary information required to proceed with the Course. Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships and their favourite restaurants.
- Access Duration
- Who is this Course for?
- Entry Requirement
- Method of Assessment
- Certification
- Awarding Body
- Career Path & Progression
The course will be delivered directly to you, and from the date you joined the course you have 12 months of access to the online learning platform. The course is self-paced, and you can complete it in stages at any time.
- Programmers, Developers, Technical Leads, Architects, Freshers
- Business Analysts
- Data Scientists, Data Analysts
- Statisticians and Analysts
- Project Managers
- Learners should be over the age of 16, and have a basic understanding of English, ICT and numeracy.
- A basic understanding of Computer Programming Languages.
In order to complete the course successfully, learners will take an online assessment. This online test is marked automatically, so you will receive an instant grade and know whether you have passed the course.
Upon the successful completion of the course, you will be awarded the ‘Python Certification Training for Data Science’ by CPD.
CPD is an internationally recognised qualification that will make your CV standout and encourage employers to see your motivation at expanding your skills and knowledge in an enterprise.
Once you successfully complete the course, you will gain an accredited qualification that will prove your skills and expertise in the subject matter. With this qualification you can further expand your knowledge by studying related courses on this subject, or you can go onto get a promotion or salary increment in your current job role. Below given are few of the jobs this certificate will help you in, along with the average UK salary per annum according to http://payscale.com/
- Computer Programmer - Up to £30;per annum
- Software Engineer - Up to £32k per annum
- Net Programmer – Up to £30k per annum
- Software Developer - Up to £24k per annum
Key Features
Gain an Accredited UK Qualification
Access to Excellent Quality Study Materials
Personalised Learning Experience
Support by Phone, Live Chat, and Email
Eligible for TOTUM Discount Card
UK Register of Learning Providers Reg No : 10053842Course Curriculum
| 1: Introduction to Python | |||
| Overview of Python | |||
| The Companies using Python | |||
| Different Applications where Python is used | |||
| Discuss Python Scripts on UNIX/Windows | |||
| Values, Types, Variables | |||
| Operands and Expressions | |||
| Conditional Statements | |||
| Loops | |||
| Command Line Arguments | |||
| Writing to the screen | |||
| 2: Sequences and File Operations | |||
| Python files I/O Functions | |||
| Numbers | |||
| Strings and related operations | |||
| Tuples and related operations | |||
| Lists and related operations | |||
| Dictionaries and related operations | |||
| Sets and related operations | |||
| 3: Deep Dive – Functions, OOPs, Modules, Errors and Exceptions | |||
| Functions | |||
| Function Parameters | |||
| Global Variables | |||
| Variable Scope and Returning Values | |||
| Lambda Functions | |||
| Object-Oriented Concepts | |||
| Standard Libraries | |||
| Modules Used in Python | |||
| The Import Statements | |||
| Module Search Path | |||
| Package Installation Ways | |||
| Errors and Exception Handling | |||
| Handling Multiple Exceptions | |||
| 4: Introduction to NumPy, Pandas and Matplotlib | |||
| NumPy – arrays | |||
| Operations on arrays | |||
| Indexing slicing and iterating | |||
| Reading and writing arrays on files | |||
| Pandas – data structures & index operations | |||
| Reading and Writing data from Excel/CSV formats into Pandas | |||
| matplotlib library | |||
| Grids, axes, plots | |||
| Markers, colours, fonts and styling | |||
| Types of plots – bar graphs, pie charts, histograms | |||
| Contour plots | |||
| 5: Data Manipulation | |||
| Basic Functionalities of a data object | |||
| Merging of Data objects | |||
| Concatenation of data objects | |||
| Types of Joins on data objects | |||
| Exploring a Dataset | |||
| Analysing a dataset | |||
| 6: Introduction to Machine Learning with Python | |||
| Python Revision (numpy, Pandas, scikit learn, matplotlib) | |||
| What is Machine Learning? | |||
| Machine Learning Use-Cases | |||
| Machine Learning Process Flow | |||
| Linear regression | |||
| Gradient descent | |||
| 7: Supervised Learning - I | |||
| What are Classification and its use cases? | |||
| What is Decision Tree? | |||
| Algorithm for Decision Tree Induction | |||
| Creating a Perfect Decision Tree | |||
| Confusion Matrix | |||
| What is Random Forest? | |||
| 8: Dimensionality Reduction | |||
| Introduction to Dimensionality | |||
| Why Dimensionality Reduction | |||
| PCA | |||
| Factor Analysis | |||
| Scaling dimensional model | |||
| LDA | |||
| 9: Supervised Learning - II | |||
| hat is Naïve Bayes? | |||
| How Naïve Bayes works? | |||
| Implementing Naïve Bayes Classifier | |||
| What is Support Vector Machine? | |||
| Illustrate how Support Vector Machine works? | |||
| Hyperparameter Optimization | |||
| Grid Search vs Random Search | |||
| Implementation of Support Vector Machine for Classification | |||
| 10: Unsupervised Learning | |||
| What is Clustering & its Use Cases? | |||
| What is K-means Clustering? | |||
| How does K-means algorithm work? | |||
| How to do optimal clustering | |||
| What is Hierarchical Clustering? | |||
| How Hierarchical Clustering works? | |||
| 11: Association Rules Mining and Recommendation Systems | |||
| What are Association Rules? | |||
| Association Rule Parameters | |||
| Calculating Association Rule Parameters | |||
| Recommendation Engines | |||
| How does Recommendation Engines work? | |||
| Collaborative Filtering | |||
| Content-Based Filtering | |||
| 12: Reinforcement Learning | |||
| What is Reinforcement Learning | |||
| Why Reinforcement Learning | |||
| Elements of Reinforcement Learning | |||
| Exploration vs Exploitation dilemma | |||
| Epsilon Greedy Algorithm | |||
| Markov Decision Process (MDP) | |||
| Q values and V values | |||
| Q – Learning | |||
| α values | |||
| 13: Time Series Analysis | |||
| What is Time Series Analysis? | |||
| Importance of TSA | |||
| Components of TSA | |||
| White Noise | |||
| AR model | |||
| MA model | |||
| ARMA model | |||
| ARIMA model | |||
| Stationarity | |||
| ACF & PACF | |||
| 14: Model Selection and Boosting | |||
| What is Model Selection? | |||
| The need for Model Selection | |||
| Cross-Validation | |||
| What is Boosting? | |||
| How Boosting Algorithms work? | |||
| Types of Boosting Algorithms | |||
| Adaptive Boosting | |||
Alexia May
The course covered even more things that i was expected. I was able to gain in-depth knowledge of Python and you will also get familiarity with data structures.
Amiyah Clark
I like the modules because they are helpful to test if I really learnt and if not go back and check where I didn’t understand.
Esmee Knight
Very good stuff for the new learners and each concept is explained in a detailed manner. Keep up the good work.