The tasks you’ll tackle in this series of liveProjects are typical of tasks a data scientist/engineer would encounter in an online recruiting tech company, a large organization’s HR department, or similar environments. You’ll develop a data pipeline for processing, extracting, and transforming various types of data to be consumed by different types of users, including machine learning engineers, data analysts, and product developers. You’ll build data processing tools with NumPy, use pandas for feature extraction and engineering, use Matplotlib to explore, visualize, and analyze processed data, and build data augmentation tools to enhance the ML modeling. By the end, you’ll have already finished 80% of the work of a typical data science project. You’ll have acquired skills, experience, and confidence that will take you closer to a career in data science.
The techniques in the building of the data processing tool were very useful. I can use that in my work projects.
As a data engineer in an online recruiting tech company or a large organization’s HR department, you’ll build a series of practical tools to process and extract useful information from unstructured text data using NumPy. You’ll learn important methods (including trie data structure, TF-IDF, SVD), how to implement them, and their applications in the real world. When you’re finished, you’ll have the know-how to build data processing tools that meet the needs of machine learning engineers, data analysts, and product developers.
Master the basic methods for handling most real-world scenarios as you play the role of a data scientist in an online recruiting tech company or a large organization’s HR department. Using pandas, you’ll process, extract, and transform numerical, categorical, time series, and text data into structured features that are ready for data analysis and ML model training. When you’re done, you’ll have hands-on experience working with most data types you’ll find in the real world, as well as useful skills for extracting and engineering features.
Visualize this: you’re a data analyst in an online recruiting tech company or a large organization’s HR department. You’ll use Matplotlib to explore, visualize, and analyze processed data to identify missing data and outliers. You’ll build interactive plots for superior data presentation, analyze the correlation of different features using visualization methods, and create analytics dashboards for two types of users. By the end, you’ll be a better data analyst and have the skills to build storytelling tools that let you answer important business questions.
As a machine learning engineer in an online recruiting tech company or HR department of a large organization, your task is to address a lack of data, a common problem in data science projects. To solve this, you’ll create multiple tools to augment processed data, increasing its volume and learning essentials about probability distributions, random sampling, and OOP. Completing this project will enhance your data analysis and visualization skills, taking you further down the path to a career in data science.
I believe people will want to purchase this project because it is an interesting topic at a good price.
These liveProjects are for Python beginners who are passionate about data and who would like to advance their careers as data analysts, data engineers, or data scientists. To begin these liveProjects you’ll need to be familiar with the following:TOOLS
In this liveProject series, you’ll learn to build data processing, data augmentation, feature extraction and engineering tools, and create interactive data analytics dashboards for storytelling.
geekle is based on a wordle clone.