This can be a super guide for you to start and excel in your data science career.
Break the ice
This can be a super guide for you to start and excel in your data science career. A lot of times we are either bombarded with inadequate information/ content which is partially industry oriented. In most cases, the effectiveness of a professional is determined on the basis of his/her practical experience. That is why one’s skill development process should involve hands-on experience of tools and languages, knowledge of enough use cases, thirst of implementing your knowledge and experience for getting a job profile in Data Science.
The pillars of data science professionals are
- Reporting tools like Excel, Google sheets
- Business/ Domain Knowledge
- SQL skills
- BI tools skills
- Python/ R skills
We are here to directly help you in understanding, practicing and implementing formulas of excel to make business reports and dashboards.
Nearly 20 domain’s real business use cases will be available for you to study and explore.
Familiarity with SQL is a must. Getting your hands more dirty in SQL will make your pockets more wealthy. If you are completely new to SQL, then it’s not a problem because we think SQL is one of the easiest languages. We will let you think, play and solve hundreds of our customized SQL exercises according to level of proficiency required.
Visualize and materialize. There is not a better way to analyze something and then present it into some beautiful, interactive visualization reports or dashboards. Tableau, Google Data Studio, Power BI these will be your friends in the process.
To give a smooth finishing touch or to build an entire platform of any data science project, python is a needed arsenal. Our team has some of the best python programmers which can help you throw away fear of python coding and make you learn some interesting python skills.
ANCOVA: Analysis of Covariance with python
ANCOVA is an extension of ANOVA (Analysis of Variance) that combines blocks of regression analysis and ANOVA. Which makes it Analysis of Covariance.
Learn Python The Fun Way
What if we learn topics in a desirable way!! What if we learn to write Python codes from gamers data !!
Meet the most efficient and intelligent AI assistant : NotebookLM
Start using NotebookLM today and embark on a smarter, more efficient learning journey!
Break the ice
This can be a super guide for you to start and excel in your data science career.
Versions of ANCOVA (Analysis Of Covariance) with python
To perform ANCOVA (Analysis of Covariance) with a dataset that includes multiple types of variables, you’ll need to ensure your dependent variable is continuous, and you can include categorical variables as factors. Below is an example using the statsmodels library in Python: Mock Dataset Let’s create a dataset with a mix of variable types: Performing…
Python Variables
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A/B Testing Quiz
Complete the code by dragging and dropping the correct functions
Python Functions
Python functions are a vital concept in programming which enables you to group and define a collection of instructions. This makes your code more organized, modular, and easier to understand and maintain. Defining a Function: In Python, you can define a function via the def keyword, followed by the function name, any parameters wrapped in parentheses,…
Python Indexing: A Guide for Data Science Beginners
Mastering indexing will significantly boost your data manipulation and analysis skills, a crucial step in your data science journey.
Diffusion Models: Making AI Creativity
Stable Diffusion Models: Where Art and AI Collide Artificial Intelligence meets creativity in the fascinating realm of Stable Diffusion Models. These innovative models take text descriptions and bring them to life in the form of detailed and realistic images. Let’s embark on a journey to understand the magic behind Stable Diffusion in a way that’s…
Quiz Challenge: Basics with Python [Questions]
Solve These Questions in Following Challange
Introducing Plethora of Stable Diffusion models: Part 1
Generate AI images as good as DALL-E completely offline.
Understanding Demand Forecasting and Addressing Common Challenges
Are demand forecasting truly predictable? Or are they changing randomly?
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