1st lesson free
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Subhashini
- Rate R430
- Response 1h
-
Students1
Number of students Subhashini has accompanied since joining Superprof
Number of students Subhashini has accompanied since joining Superprof
R430/h
Contact
1st lesson free
- Python
Join case study-driven Python course for Data Science, ML, and Analytics. I have 2 classes : 1. Project based Learning for young professionals 2. Python for school
- Python
Lesson location
About Subhashini
Subhashini Sharma Tripathi is a renowned subject matter expert with 20 years of experience in the fields of Analytics and Data Science. During the initial stage of her career, she held key positions in esteemed companies like GE Money, Standard Chartered, Tata Motors Finance, and Citi GDM, where she focused on Risk Analytics and Business Intelligence, excelling in team lead roles.
In the subsequent years, Subhashini ventured into Analytics Education and co-founded Pexitics (People Excellence Indicator Analytics). She specializes in Career Guidance and Advisory for young people - high school o professionals - using the machine learning tool developed inhouse.
Subhashini's expertise extends to conducting Corporate Knowledge sessions for notable organizations like Genpact, Credit Suisse, HP, Infosys, and others. She also played a significant role in setting up course content and delivery for post-graduate courses in Analytics at various institutions, including Symbiosis University, Jigsaw Academy, and Manipal University.
As an author, Subhashini has written two publications, "Learn Business Analytics in 6 steps using SAS and R" and "HR Analytics – In Depth," showcasing her knowledge in the field. She is well-versed in using several software tools such as SAS, R, Python, Excel, SPSS, and Tableau.
Currently, Subhashini is actively involved in Pexitics Assessments and Surveys, a platform for Talent assessment, Intelligence Amplification, and Decision Support in People Decisions. Additionally, she undertakes Analytics and Data Science projects, working on Credit Risk Models, NLP models, Sentiment Analytics, and more.
Subhashini is based in Bangalore, India, and can be reached via LinkedIn, email, or phone for those interested in connecting or seeking her expertise in the domain of Data Science and Analytics.
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About the lesson
- All levels
- English
Languages in which the lesson is available :
English
I am sharing some general guidance on learning Python for Data Science, Machine Learning, and Analytics for BOTH the courses.
Note: Customized classes can also be taken on request.
1. Project-based Learning for Young Professionals:
If you are already familiar with Python basics, it's great to start focusing on practical projects that will help you apply your knowledge in real-world scenarios. Here are some steps you can follow:
a. Data Science Fundamentals: Brush up on essential data science concepts like data manipulation, visualization, and statistical analysis using libraries like Pandas, NumPy, and Matplotlib/Seaborn.
b. Machine Learning Basics: Learn about different machine learning algorithms, their applications, and how to implement them using libraries like Scikit-learn.
c. Case Studies: Work on various case studies and projects that involve real datasets. You can find datasets from sources like Kaggle, UCI Machine Learning Repository, or government data portals.
d. Data Visualization: Improve your data visualization skills using libraries like Matplotlib, Seaborn, and Plotly to create compelling visuals.
e. Deep Learning: If you're interested in deep learning, you can delve into neural networks using TensorFlow or PyTorch.
f. Data Storytelling: Learn how to communicate your findings effectively through data storytelling and data presentations.
2. Python for School:
Python is an excellent language for beginners, and it's widely used in educational settings. Here's a suggested learning path:
a. Python Basics: Start with the basics of Python programming, including variables, data types, loops, and conditional statements.
b. Functions and Modules: Learn how to create functions and use Python modules to organize your code.
c. Data Structures: Understand lists, dictionaries, tuples, and sets to work with different types of data.
d. File Handling: Learn how to read and write data to files, which is crucial for various school projects.
e. Introduction to Data Science: Introduce basic data analysis techniques using Pandas library, including data manipulation and visualization.
f. Simple Projects: Encourage students to work on simple projects like creating a calculator, building a text-based game, or solving math problems using Python.
g. Web Scraping: For more advanced students, an introduction to web scraping can be fascinating.
h. Visualization: Introduce basic data visualization using libraries like Matplotlib or Plotly.
Remember, hands-on practice and working on real projects is essential for mastering Python and data-related fields. So, ensure that you get involved in practical exercises and case studies to reinforce your learning. Additionally, there are many online resources like tutorials, blogs, and interactive coding platforms to aid your learning journey.
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Rates
Rate
- R430
Package rates
- 5h: R2150
- 10h: R4300
online
- R430/h
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The free first lesson with Subhashini allows you to get to know the tutor and discuss your needs and expectations.
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