Python for Data Science Certification Training
The EdUnbox Python for Data Science training course helps you to master the various concepts of such a widely utilized and efficient programming language, like Python. Students will receive world-class training and hands-on learning of various python packages such as SciPy, Matplotlib, NumPy, as well as Lambda function, and much more. The course also enables students to work on real-world projects in Python and applications in a vast number of associated domains of Data Science, Big Data, plus Machine Learning.
About Data Science With Python Training Course:
This EdUnbox Data Science with Python training course establishes data science & analytics techniques mastery utilizing Python. Using Python for Data Science Course, students learn the fundamental Python programming concepts and get in-depth knowledge in terms of data analytics, data visualization, machine learning, natural language processing, and web scraping. This hands-on interactive course is perfect. Data scientists requiring Python skills for tapping employment opportunities would benefit from this course. Now acquire knowledge about Python from scratch. EdUnbox’s Data Science with Python Course also equips in the mastery of essential and mission-critical Python programming conceptions including data & file operations object-oriented programming, besides Python libraries including Pandas, Matplotlib and Numpy vital for Data Science.EdUnbox’s Data Science with Python training course is your key to unlocking data science skills.
What will you learn in the Data Science With Python Training course?
This EdUnbox Data Science with Python training course will enable deep learning of the top programming language in Data Science. Students will learn and gain mastery regarding the technique used for Python for Data Science. They will also acquire knowledge about libraries, data munging, data cleaning, data visualization, web scraping and so on. This course covers the following topics:
- Introduction to Data Science with Python
- Creating Hive UDF and Pig in Python
- Deploying Python towards MapReduce programming
- OOP concepts, functions and expressions
- Real-world Data Science with Python projects
- SQLite in Python, classes, and operations
Who should go for this Data Science With Python training?
There is increasing demand for skilled data science professionals across different industries making the course ideal for participants to varying levels of critical experience. So, the course can benefit the following professionals:
- Analytics professionals looking to work on Python
- Software or IT professionals keen to pursue analytics
- Graduates who want a career in analytics & data science and those interested in data science
- Programmers, Developers, Technical Leads, Architects, Machine learning expert
- Analytics Managers, Business Analysts, and Python professionals seeking further skill-building
What are the prerequisites for this Data Science With Python training?
Basic understanding of Computer Programming Languages is an added advantage. Fundamentals of Data Analysis should be clear.
- Forbes has named data science as the best job in the US for 2018, and the median base salary of $242,000 apart, there are 4,524 job openings.
- TIOBE index estimates Python is among the most well-known, popular and used programming languages globally
- Python’s libraries & designs provide 10x times the productivity as against C, C++ or Java
- A Senior US-based Python Developer can earn $102,000, according to job search siteindeed.com
1. Introduction to Data Science with Python
- Overview of Python
- Python Scripts on different UNIX/Windows
- Command Line type Arguments
- Companies utilizing Python
- Conditional Statements
- Different Applications of Python usage
- Operands & Expressions
- Values Variables & Types
- Writing to screens
2. Sequences & File Operations
- Dictionaries & related operations
- Lists as well as related operations
- Sets & related operations
- Python files & I/O Functions
- Strings plus related operations
- Tuples along with related operations
3. Deep Dive into functions, Modules, OOPs Errors & Exceptions
- Errors & Exception Handling as well as Multiple Exceptions Handling
- Function-Based Parameters
- Global-Type Variables
- Lambda Functions
- Module Search Path
- Modules Deployed in Python
- Object-Oriented Concepts
- Standard Libraries
- The Import Statements
- Variable Scope & Returning Values
- Ways of Installation of Package
4. NumPy, Pandas & Matplotlib: An Introduction
- arrays Operations
- Grids, plots, axes
- Indexing slicing & iterating
- Matplotlib library
- NumPy – arrays
- Pandas – data structures and index operations
- Reading & writing arrays on the files
- Reading plus Writing data using Excel/CSV formats for Pandas
- Markers, colors, fonts & styling
- Kinds of plots – bar graphs, histograms, and pie charts
- Contour plots
5. Data Manipulation
- Analysis of a dataset
- Concatenation for data objects
- Exploration of a Dataset
- Merging of Data objects
- Types of Joins related to data objects
- Data object: Key Functionalities
6. Introduction: Machine Learning using Python
- Concepts of Machine Learning
- Linear regression & Gradient descent
- Machine Learning Based Process Flow
- Machine Learning Based Use-Cases
- Machine Learning Type Categories
- Python Revision (Pandas, Numpy, Matplotlib, Scikit learn)
- Gradient descent
7. Supervised Learning: Basic Concepts
- Algorithms of Decision Tree Induction
- Confusion Matrix
- Creating the Perfect Decision Tree
- What are a Classification and the use cases?
- What is a Decision Tree?
- Fundamentals of Random Forest?
8. Dimensionality Reduction
- Factor Analysis
- Introduction: Dimensionality
- PCA & LDA
- Reasons for Dimensionality Reduction
- Scaling dimensional model
9. Supervised Learning Advanced
- Grid Search versus Random Search
- How does Naïve Bayes work?
- Hyperparameter Optimization
- Implementing the Naïve Bayes Classifier
- Applying support Vector Machine for Classification
- Naïve Bayes: Basic Concepts
- What is the Support Vector Machine?
- Working of Support Vector Machine
10. Unsupervised Learning
- Clustering Use Cases
- C-means Clustering
- Hierarchical Clustering
- How does Hierarchical Clustering work?
- K-means Clustering
- Optimal clustering
- What does Clustering mean?
- Working of the K-means algorithm
11. Association Rules Mining Plus Recommendation Systems
- Calculation of Association Rule Parameters
- Collaborative Filtering
- Content-Based Filtering
- Recommendation Engines & Their Working
- Basics of Association Rules
- Parameters of Association Rule
12. Reinforcement Learning
- What and how does Reinforcement Learning work?
- Elements for Reinforcement Learning
- Epsilon Greedy Algorithm
- Exploration versus Exploitation dilemma
- MDP/Markov Decision Process
- Q – Learning
- Q values & V values
- Why choose Reinforcement Learning
13. Time Series Analysis
- ACF and PACF
- Fundamentals of Time Series Analysis
- Importance & Components of TSA
- The AR, MA, ARIMA and ARMA model
- White Noise
14. Model Selection & Boosting
- Adaptive Boosting
- Reasons for Model Selection
- Types of Boosting Algorithms & Adaptive Boosting
- Understanding Boosting
- Understanding Boosting Algorithms
- Working on Cross-Validation
- Model Selection: Basic Concepts
EdUnbox is delighted to offer this comprehensive course for clearing the Python for Data Science Certification. Now, get the best positions in private companies, MNCs and PSUs with this useful qualification. As part of the training, we are also offering real-time assignments and projects that have amazing implications in the real-world industry scenario helping to accelerate your career effortlessly.
Towards the completion of this training program, you will participate in the real-time projects and quizzes that will prepare you for questions in the certification examination and help you to score well in this exam. EdUnbox Course Completion Certificate will be awarded on the completion of projects on the basis of trainer reviews and on scoring 50% minimum marks in the quiz.
EdUnbox certification is well recognized among leading corporate brands in a wide range of industries and verticals including Fortune 500 companies. Let the community know about your achievement and become certified today! Advance your career with our Data Science With Python Training course.
Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.
- Visa Credit or Debit Card
- American Express
- Diner’s Club
I loved the way Edunbox trainers taught the Python programming language as applicable to the Data Science domain. Great work!
the instructor has very good knowledge of data science with python. Thank you Edunbox
I learned a lot. Thank you team Edunbox….
Excellent support team and great teaching process.
Now i am confident in data science with python…good job Edunbox
I am more than happy with the course. Looking forward to learn more Edunbox online training courses.
I definately want to recommend this course, very good course to move in data science.Thanks
I am happy that Edunbox provides flexible scheduling of classes. They gave immense support during my classes. I am happy with the course material and instructor’s way of teaching as well. I definitely recommend this course to everyone.
Feeeling happy after complete this course.Great……Edunbox
I got very good experience with the real-time projects provided by Edunbox. The trainer was top class. The industry experience he carries is awesome.
learning process was very very well managed.Thank you so much. Edunbox
I believe this is the best course for beginners.
This instructor-led training course is a beginner basic to advance in career. The trainer taught me all concepts from scratch. The study material really helped me to understand the subject properly.
I loved the support team…Edunbox
I liked the dedication of the Edunbox support team when it came to resolving my queries regardless of the time of the day. Hats off to team Edunbox!