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- The impact of data science and analytics

Data science and analytics are rapidly shaping every aspect of our lives and our businesses. We’re collecting more data than ever before, but not everyone is able to efficiently analyze all that data to extract meaningful insights. There is incredible power in data—but only if you know what to do with it. DataCamp teaches companies and individuals the skills they need to work with data in the real world.

- Skill up at scale. Data training designed for your business.

Join 2,500+ companies and 80% of the Fortune 1000 who use DataCamp to upskill their teams.

A.) Course Description: Introduction to Python

An Introduction to Python

Python has grown to become the market leader in programming languages and the language of choice for data analysts and data scientists. Demand for data skills is rising because companies want to gain actionable insights from their data.

Discover the Python Basics

This is a Python course for beginners, and we designed it for people with no prior Python experience. It is even suitable if you have no coding experience at all. You will cover the basics of Python, helping you understand common, everyday functions and applications, including how to use Python as a calculator, understanding variables and types, and building Python lists. The first half of this course prepares you to use Python interactively and teaches you how to store, access, and manipulate data using one of the most popular programming languages in the world.

Explore Python Functions and Packages

The second half of the course starts with a view of how you can use functions, methods, and packages to use code that other Python developers have written. As an open-source language, Python has plenty of existing packages and libraries that you can use to solve your problems.

Get Started with NumPy

NumPy is an essential Python package for data science. You’ll finish this course by learning to use some of the most popular tools in the NumPy array and start exploring data in Python.

B.) Course Description: Intermediate Python

Improve Your Python Skills

Learning Python is crucial for any aspiring data science practitioner. Learn to visualize real data with Matplotlib’s functions and get acquainted with data structures such as the dictionary and pandas DataFrame. This four-hour intermediate course will help you to build on your existing Python skills and explore new Python applications and functions that expand your repertoire and help you work more efficiently.

Learn to Use Python Dictionaries and pandas

Dictionaries offer an alternative to Python lists, while the pandas dataframe is the most popular way of working with tabular data. In the second chapter of this course, you’ll find out how you can create and manipulate datasets, and how to access them using these structures. Hands-on practice throughout the course will build your confidence in each area.

Explore Python Boolean Logic and Python Loops

In the second half of this course, you’ll look at logic, control flow, filtering and loops. These functions work to control decision-making in Python programs and help you to perform more operations with your data, including repeated statements. You’ll finish the course by applying all of your new skills by using hacker statistics to calculate your chances of winning a bet.

Once you’ve completed all of the chapters, you’ll be ready to apply your new skills in your job, new career, or personal project, and be prepared to move onto more advanced Python learning.

C.) Course Description: Python Data Science Toolbox (Part 1)

Writing your own functions

In this chapter, you'll learn how to write simple functions, as well as functions that accept multiple arguments and return multiple values. You'll also have the opportunity to apply these new skills to questions commonly encountered by data scientists.

Default arguments, variable-length arguments and scope

In this chapter, you'll learn to write functions with default arguments so that the user doesn't always need to specify them, and variable-length arguments so they can pass an arbitrary number of arguments on to your functions. You'll also learn about the essential concept of scope.

Lambda functions and error-handling

Learn about lambda functions, which allow you to write functions quickly and on the fly. You'll also practice handling errors in your functions, which is an essential skill. Then, apply your new skills to answer data science questions.

D.) Course Description: Python Data Science Toolbox (Part 2)

Using iterators in PythonLand

You'll learn all about iterators and iterables, which you have already worked with when writing for loops. You'll learn some handy functions that will allow you to effectively work with iterators. And you’ll finish the chapter with a use case that is pertinent to the world of data science and dealing with large amounts of data—in this case, data from Twitter that you will load in chunks using iterators.

List comprehensions and generators

In this chapter, you'll build on your knowledge of iterators and be introduced to list comprehensions, which allow you to create complicated lists—and lists of lists—in one line of code! List comprehensions can dramatically simplify your code and make it more efficient, and will become a vital part of your Python data science toolbox. You'll then learn about generators, which are extremely helpful when working with large sequences of data that you may not want to store in memory, but instead generate on the fly.

Bringing it all together!

This chapter will allow you to apply your newly acquired skills toward wrangling and extracting meaningful information from a real-world dataset—the World Bank's World Development Indicators. You'll have the chance to write your own functions and list comprehensions as you work with iterators and generators to solidify your Python data science chops.

E.) Course Description: Supervised Learning with scikit-learn

Grow your machine learning skills with scikit-learn and discover how to use this popular Python library to train models using labeled data. In this course, you'll learn how to make powerful predictions, such as whether a customer is will churn from your business, whether an individual has diabetes, and even how to tell classify the genre of a song. Using real-world datasets, you'll find out how to build predictive models, tune their parameters, and determine how well they will perform with unseen data.

Classification

In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Regression

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Fine-Tuning Your Model

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

Preprocessing and Pipelines

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Acquired proficiency in arrays, methods, lists, list comprehensions, dictionaries, pandas’ data frames, matplotlib, functions, lambda functions, tuples, logic, control flow statements, filtering, loops, data entry, and visualization. Furthermore I dealt with functions of zero arguments, 1 argument, multiple arguments, default and flexible arguments, *args, **kwargs, the map function, the filter function, generators, iterators, zip, enumerate and unpack, advanced comprehensions, etc.

When it comes to the supervised learning statement of accomplishment, I acquired general descriptive competencies over the following tools:

K Neighbors Classifier - Model Complexity / Overfitting-Underfitting - Accuracy / Computing Accuracy - Train/Test Split Data - Regression: Linear Regression - Regression, Theory & Metrics (R^2, RMSE) - KFold Cross Validation - Regularized Regression: Ridge, Lasso - Ridge Loss Function - Lasso Loss Function - Feature Selection in scikit-learn - Fine Tuning Your Model / Confusion Matrix - Harmonic Mean of Numbers - Revising Logistic Regression

Received Certificates so far:

1.) Introduction to Python Statement of Accomplishment
2.) Intermediate Python Statement of Accomplishment
3.) Python Data Science Toolbox Part 1 Statement of Accomplishment
4.) Python Data Science Toolbox Part 2 Statement of Accomplishment
5.) Supervised Learning with scikit-learn Statement of Accomplishment

In the future there might be future specializations in Python such as concrete analytics tools or targeted areas of further studying, but that depends only on the saturation of previous knowledge.

Source: DataCamp