Data Science Methods and Algorithms [2025] from Udemy

What's inside

Learning objectives

Knowledge about data science methods, algorithms, theory, best practices, and tasks
Deep hands-on knowledge of data science and know how to handle common data science tasks with confidence
Detailed and deep master knowledge of regression, prediction, classification, supervised learning, cluster analysis, and unsupervised learning
Hands-on knowledge of scikit-learn, statsmodels, matplotlib, seaborn, and some other python libraries
Advanced knowledge of a.i. prediction models and automatic model creation

Cloud computing: use the anaconda cloud notebook (cloud-based jupyter notebook). learn to use cloud computing resources
Option: to use the anaconda distribution (for windows, mac, linux)
Master the python 3 programming language for data handling
Master pandas 2 and 3 for advanced data handling

Knowledge about data science methods, algorithms, theory, best practices, and tasks
Deep hands-on knowledge of data science and know how to handle common data science tasks with confidence
Detailed and deep master knowledge of regression, prediction, classification, supervised learning, cluster analysis, and unsupervised learning
Hands-on knowledge of scikit-learn, statsmodels, matplotlib, seaborn, and some other python libraries
Advanced knowledge of a.i. prediction models and automatic model creation
Cloud computing: use the anaconda cloud notebook (cloud-based jupyter notebook). learn to use cloud computing resources
Option: to use the anaconda distribution (for windows, mac, linux)
Master the python 3 programming language for data handling
Master pandas 2 and 3 for advanced data handling

Syllabus

Introduction to Data Science Methods and Algorithms

Introduction and overview of the course

This video describes the setup procedures for using the Anaconda Cloud Notebook

Using Anaconda Cloud Notebook requires internet access and an email address

Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures

This video describes the procedures to download and install the Anaconda Distribution for use with this course

Download requires internet access

Video is optional

Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures

This video describes the Conda Package Management System

Conda requires internet access

Video is optional

Note: Conda is a speedily developing environment and this may cause minor differences in graphics and procedures

This video provides an overview of "Python for data handling", teaches you some Python and Data Handling theory, and presents a table of contents for Python for Data Handling as well as some basic information about the Jupyter IDE with dynamic typing, Python programs organization, and some fundamental Python language syntax

Learn to use Python Integers

Learn to use Python Floats

Learn to use Python Strings

Learn to use some Python string methods to test, search, transform, change, and manipulate string data

Learn to use date and time data with Python's Datetime module. Learn to calculate time durations and time event data. Learn advanced knowledge about date and time data plus how computers and Python handle datetime data

This video provides an overview of the part of this section about Python's data storage abstractions, the set, tuple, dictionary, and the list

Learn to use Python's Set

Learn to use Python's native Tuple and how to unpack Tuples

Learn to use Python's native Dictionary

Learn to use Python's native List

An overview of the contents of this subpart of the section, Python's data transformers, and functions

Learn to use Python's native while-loop with some practical examples

Learn to use Python's native for-loop with some practical examples

Learn to use some of Python's logic operators and conditional code branching. Use your learned knowledge to edit and tailor basic descriptive statistics at a detailed level

This video lecture describes the theoretical advantages of Python's functions

Learn practical coding with Python's functions. You are introduced to functions and basic protections for functions. You will learn how to create functions from code-examples from earlier video lectures, and you will learn how to generalize functions up to advanced uneven-multitype-object 2-dimensional list of lists

Learn to create your own functions!

Learn Python OOP theory relevant for data handling tasks and how object-oriented data structures may affect data handling

Learn to code object-oriented programming with Python, and to handle Python object-oriented code and custom objects within the ambit of data handling

Learn to save files in Python and the practical process of converting custom Python objects to tabular form and saving these into .csv, and Excel files and to load files to Pandas Data Frames

This video lecture is a recap and extension of earlier video lectures. You will assemble knowledge from earlier lectures into more powerful knowledge. You will learn to construct a tabular data form with additional calculated variables and how to use the tabular data form for plotting, etc. You will learn how Data Handling fits with advanced object-oriented program structures.

This video provides and introduction and overview of this section of the video course. "Master Pandas for Data Handling" is updated to current Pandas 2.2 and all known new changes in the future Pandas 3 version.

Learn the fundamental concepts and language of the Pandas DataFrame, the Pandas Series, and the data or object content of a DataFrame/Series object.

Learn to create Pandas DataFrame from scratch using Python and Pandas. You will learn how to create Pandas DataFrames using Python Dictionaries, Lists, and lots more.

This video contains an overview of the Pandas File Handing part of this section.

Learn to load and save files from/to Pandas DataFrames from .csv files.

Learn to load and save files from/to Pandas DataFrames from .xlsx files and hierarchical .xlsx files.

Learn to load and save files from/to Pandas Dataframes from a SQL-database file.

This video contains an overview of the Pandas Operations and Techniques part of this section.

Learn to inspect Pandas Dataframes and Dataframe content with Pandas .info() method, Python's .type() method, and more

Learn to inspect the contents of large-sized Pandas DataFrames. Learn to use the .head, .tail, and other general methods to inspect the contents of a DataFrame

Learn to select subsets of Columns from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame

Learn to select subsets of Rows from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame

Learn to make conditional selections of subsets from a Pandas DataFrame. Learn to use the .loc and .iloc functions to select subsets from a Pandas DataFrame

Learn about Scalers, Normalization, and Standardization. Learn to use mean-correction, normalization, and zero-one unity-based normalization

Learn to Concatenate Pandas DataFrames. Learn to use Pandas .concat() function to add DataFrames together horizontally and vertically. Learn to use the .concat() function with Inner and Outer joins

Learn to join Pandas DataFrames. Learn to use Pandas DataFrames .join() method. Learn to use "left joins", "right joins", "inner joins", "outer joins", and "cross joins"

Learn to merge Pandas DataFrames. Learn to use Pandas DataFrames .merge() method. Learn to use "left joins", "right joins", "inner joins", and "outer joins" to merge different DataFrames on column variables

Learn to Transpose and Pivot Pandas DataFrames. Learn to use the transpose, pivot, pivot_table, and melt functions

This video has an overview of the Data Preparation part of the course and includes a workflow for Data preparation or so-called data cleaning

Learn to edit Pandas DataFrame column names, index, and index labels

Learn about Duplicates. Duplicate rows or observations may impact the quality of data products. Learn how to properly handle Duplicates with Pandas functionality

Learn to handle Missing data and Missing values with Pandas functionality. Learn Imputation and to augment Pandas with scikit-learn to use advanced model-based imputation of missing data

Learn Data Binning with Pandas. Learn to use Administrative Data Binning, Algorithmic Data Binning, and subjective Data Binning. Learn to use Pandas .qcut() and .cut() functions.

Learn to create Indicator Features or Dummy Features with Pandas

This video provides an overview of the part of this section about Pandas Data Description

Learn to use Pandas functions for Sorting and Ranking data

Learn to create useful descriptive statistics with Pandas .agg() and .describe() functions. Learn to augment Pandas functions with the powerful .apply() and .value_counts() functions

Learn to create crosstabulations with Pandas .crosstab() function and to use the powerful Pandas .groupby() operation. Learn to augment these functions with a selection of Pandas functionality

This video contains an overview of Pandas Data Visualization and gives an overview of the contents of this part of the section Master Pandas for Data Handling

Learn to make Histograms with Pandas, Matplotlib, and Seaborn. You will learn to make simple Histograms, advanced Histograms, multi-dimensional Histograms, and advanced Jointgrid Histograms

Learn to make traditional and modern Boxplots with Pandas, Matplotlib, and Seaborn. You will learn to make Boxplots, Boxenplots, Violinplots, Swarmplots and to create graphs consisting of many types of boxplots

Learn to make scatterplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple scatterplots, advanced scatterplots, advanced multi-scatterplots, and advanced pairplots of scatterplots

Learn to make Pie Charts with Pandas, Matplotlib, and support from Seaborn. You will learn to make Pie Charts, detailed Pie Charts, multiple Pie Charts, and how to properly use Pie Charts for effect

Learn to make Lineplots with Pandas, Matplotlib, and Seaborn. You will learn to make simple Lineplots, advanced Lineplots, advanced Line-area plots, and advanced multidimensional Line-area plots

This video provides an overview of this section with a table of contents. The concepts of Regression, Prediction, and Supervised Learning are described

Learn to use the traditional simple regression model, some fundamental theory and to create a regression model in a theoretically correct environment with the Scikit-learn and Statsmodels libraries

Learn to use the traditional simple regression model, more fundamental theory, and tools to check and inspect model-fit-to-data, and model assumptions. Learn to create powerful residual plots with Pandas and Matplotlib, and learn to use the R-squared and Durbin-Watson statistics from the Statsmodels summary output

Learn some practical and useful modeling concepts. Learn about Overfitting, Underfitting, and the Bias-Variance tradeoff

Learn some practical and useful modeling concepts. Learn to use Generalizations with Interpolation and extrapolation. Learn about model interpretation and learn about the fake sample or non-causality concept and about simple or advanced models

Create a Linear Multiple Regression Model using correlation matrixes and heatmaps. Learn model Diagnostics and Residual Analysis using both standard package Residual plots and more advanced designed Residual plots

Deepen your knowledge about Linear Multiple Regression Models. Introduction to Machine Learning Automatic Model Creation with Forward Selection and Probability-Values

Learn theory about Multivariate Polynomial Regression Models and Regression terminology. Learn some theory about Automatic model creation (AI) using Machine Learning backward elimination and Regression Models

Learn to code Multivariate Polynomial Multiple Regression Models combined with the Backward Elimination Feature Selection Algorithm for Machine Learning Automatic Model Creation. Learn to make Feature transformations, Residual Analysis, and some about how to plot advanced high-dimensional model predictions in low dimensional spaces, in a simplified fashion

Learn about Regularization and to Regularize regression models using Lasso and Ridge Regression. Example regularizing an overfit Polynomial Multiple Regression Model

Learn Decision Tree Regression theory and to implement and regularize Decision Tree Regression models with Scikit-learn. Learn to prepare a dataset for use with Decision Tree Regression models and how to plot Decision Tree graphs and the output of Decision Tree Regression models

Learn to use Random Forest Regression / Ensembles for Prediction and Regularization. Learn to use importances for model creation and feature selection. Learn how importances change over different subsets of a dataset

Learn to use the Voting Ensemble Regression model for prediction. Learn to use Voting Regression to augment and modify standard Regression models for extended functionality and advanced prediction

An overview of the Classification section of the video course. A description of the Classification theory and process

Learn to use the Logistic Regression Classifier with a practical example, learn to create advanced decision surface plots, use exploratory seaborn pair plots, and learn to create useful classification reports and much more…

Learn to use the Naive Bayes Classifier. Learn some about Bayes theorem, conditional probability, model extrapolations, data quality effect on accuracy, practical modeling theory and more…

Learn to use K-Nearest Neighbor Classifier (KNN). Learn to use heuristics and graphs to determine a useful number of neighbors and learn practical hands-on classification skills for datasets with complex data structures

Learn to use the Decision Tree Classifier. Learn to Visualize Decision trees and to create corresponding Decision Surfaces.

Learn some tricks to enhance Decision Tree Classifiers performance and more...

Learn to use the Random Forest Classifier. Learn some theory about Random Forest Classifiers and importances. Learn to extract Decision Trees from a Random Forest and learn to graph importances and decision surfaces

Learn to use Linear Discriminant Analysis (LDA). Learn to use permutation importances for feature selection to overcome the complexity of environments with many features.

Learn to use ROC-curves, DET-curves, Precision-Recall graphs, and more…

Learn to use the Voting Classifier Ensemble. Learn to use the Voting Classifier as a tool to create almost arbitrary decision surfaces, Classification models, and more...

This video provides an overview of the Master Cluster Analysis and Unsupervised Learning section, and some theory on Cluster Analysis and Unsupervised Learning

Learn to use K-Means Cluster Analysis in a deep, practical and hands-on fashion. Learn to use practical and useful knee/elbow inertia plots and silhouette score plots. Use visualization tools to compare K-Means Cluster Analysis with subject matter expert classifications on a dataset.

Extend your knowledge about K-means Cluster Analysis to Auto-updated / prototyped simulations. Learn some about the most important and defining tasks within machine learning and data science. Gain understanding about concepts such as truth, predicted truth, and model-based conditional truth.

Learn about data quality, model quality, practical data analysis, simulations and some new ways to study and graph Cluster Analysis models.

Density-Based Spatial Clustering of Applications with Noise (DBSCAN). An exploratory analysis searching for data structures in the sized California Housing Dataset

Hierarchical Cluster Models. The Ward, Single, Average, and Complete linkage models. Dendrogram graphs for small-sized datasets. Exploratory analysis searching for structures in the California Housing Dataset

Learn to use Principal Component Analysis in a practical and hands-on fashion with some theory. Learn to use Principal Components as a technique for data transformations and dimensionality reduction

Learn to make Scree plots, heatmaps, and Indices plots plus learn to use these plots for component selections and dimensionality reduction. Learn to create uncorrelated Principal Component Loading to augment supervised learning models

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on Pandas and Python, which are essential tools for data handling and manipulation, potentially increasing productivity for data scientists and machine learning engineers

Provides a comprehensive teaching package for mastering Python and Pandas, making it accessible to individuals with no prior knowledge of programming or data science

Covers a wide range of data science methods and algorithms, including regression, classification, and cluster analysis, providing a solid foundation for various data science tasks

Includes hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, and Seaborn, which are widely used Python libraries for data science and machine learning

Teaches Pandas 2 and prepares learners for Pandas 3, but learners should note that the field is rapidly changing and newer versions may soon be available

Requires learners to install Anaconda distribution or use Anaconda Cloud Notebook, which may require additional setup steps and familiarity with cloud computing environments

Reviews summary

Comprehensive data science methods with python & pandas

According to learners, this course offers comprehensive coverage of data science methods, including regression, classification, and clustering, with a strong focus on practical application using Python and up-to-date Pandas versions. Many appreciate the hands-on coding examples and the use of popular libraries like Scikit-learn and Statsmodels. However, some students find the pace to be fast and the explanations for complex theoretical concepts can be dense, occasionally requiring external resources. There's also a consensus that despite being advertised for beginners, the course is likely better suited for learners with some prior programming or data handling experience.

Best suited for learners with some coding background.

"It's advertised for beginners, but I struggled as a complete novice. Needs clearer prerequisites or more foundational content."

"Disappointing... Not suitable for true beginners despite the claim. Felt more like a survey than a deep dive."

"The 'beginner' claim seems questionable. Better for those with some background."

Includes recent library versions & techniques.

"The material is up-to-date (Pandas 2/3 mentioned!)."

"This video provides and introduction and overview of this section... updated to current Pandas 2.2 and all known new changes in the future Pandas 3 version."

Excellent foundation in data handling.

"Excellent course! Covers a vast amount of material from Python/Pandas basics to advanced ML algorithms..."

"The Python and Pandas sections are solid."

"Highly recommend! ... The data handling with Pandas is explained in detail."

"A solid introduction to data science methods. I appreciated the depth in Pandas for data handling, which is crucial."

Covers many essential DS methods & tools.

"Excellent course! Covers a vast amount of material from Python/Pandas basics to advanced ML algorithms like Random Forests and DBSCAN. The hands-on coding examples are very helpful."

"Very comprehensive course. It touches on almost every core data science concept. I found the explanations for some of the ML algorithms... Good value for the money..."

"Highly recommend! This course is packed with information. The practical examples using Scikit-learn and Statsmodels are fantastic. It really delivers on the promise..."

"A solid introduction to data science methods. I appreciated the depth in Pandas for data handling, which is crucial. The ML sections provide good overviews and code examples."

Some complex theories explained too fast.

"I found the explanations for some of the ML algorithms, especially the theoretical parts, a bit dense and required external resources to fully grasp."

"The course covers a lot of ground, maybe too much. I felt rushed through some topics... Some explanations were hard to follow without prior knowledge."

"Disappointing... The explanations can be confusing, and I found myself relying heavily on external sources."

"The pace is fast. Make sure you are comfortable with basic programming before starting."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science Methods and Algorithms [2025] with these activities:

Review Python Fundamentals

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Solidify your understanding of Python fundamentals before diving into data science applications. This will make learning Pandas and other libraries much smoother.

Browse courses on Python Basics

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Review basic data types (integers, floats, strings, booleans).
Practice writing simple functions and control flow statements (if/else, loops).
Work through online Python tutorials or exercises.

Brush Up on Pandas Basics

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Familiarize yourself with Pandas DataFrames and Series. This will help you understand the data handling techniques taught in the course.

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Review how to create DataFrames from dictionaries and lists.
Practice selecting, filtering, and modifying data in DataFrames.
Explore basic Pandas functions like `head()`, `tail()`, and `describe()`.

Read 'Python for Data Analysis' by Wes McKinney

Show steps

Supplement your learning with a comprehensive guide to Pandas. This book provides in-depth explanations and practical examples.

View Python for Data Analysis on Amazon

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Read the chapters on data cleaning and transformation.
Work through the examples related to data aggregation and grouping.
Experiment with the techniques on your own datasets.

Four other activities

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Show all seven activities

Complete Pandas Data Manipulation Exercises

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Reinforce your Pandas skills through targeted exercises. This will help you become more proficient in data manipulation.

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Find online resources with Pandas exercises (e.g., Kaggle, HackerRank).
Focus on exercises that involve filtering, grouping, and merging data.
Review your solutions and identify areas for improvement.

Analyze a Real-World Dataset

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Apply your data science skills to a real-world problem. This will solidify your understanding of the entire data science pipeline.

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Choose a dataset from a public repository (e.g., Kaggle, UCI Machine Learning Repository).
Clean and preprocess the data using Pandas.
Perform exploratory data analysis (EDA) to identify patterns and insights.
Build and evaluate a predictive model using Scikit-learn.

Write a Blog Post on a Data Science Topic

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Deepen your understanding by explaining a data science concept in your own words. This will force you to think critically about the material.

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Choose a specific data science topic covered in the course.
Research the topic thoroughly and gather relevant information.
Write a clear and concise blog post explaining the concept.
Include examples and visualizations to illustrate your points.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow' by Aurélien Géron

Show steps

Expand your knowledge of machine learning algorithms and techniques. This book provides practical examples and code implementations.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters on regression, classification, and clustering.
Work through the code examples and experiment with different datasets.
Explore the advanced topics such as neural networks and deep learning.

Career center

Learners who complete Data Science Methods and Algorithms [2025] will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist uses programming and statistical techniques to analyze data, find patterns, and make predictions. This course in Data Science Methods and Algorithms builds a foundation for this role by covering essential topics such as regression, classification, cluster analysis, and supervised and unsupervised learning. The course's focus on Python and Pandas for data handling is especially relevant as data scientists spend significant time preparing data for analysis. Mastery of these tools enables a data scientist to be efficient and effective. This course provides hands-on experience with these tools, which is critical for success as a Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine learning engineers design, build, and deploy machine learning models and systems. This course, with its focus on regression, classification, and clustering, provides the fundamental knowledge and skills needed for a machine learning engineer. The course's hands-on approach with libraries like Scikit-learn and Statsmodels, alongside the in-depth knowledge of techniques like model building, feature selection, and artificial intelligence, will help a machine learning engineer to build robust models. The emphasis on Python, Pandas, and data handling is also critical for a Machine Learning Engineer who spends considerable time preparing data for these models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data analysts examine data sets to identify trends, develop reports, and make data-driven recommendations. A course like this one, which provides an introduction to data science methods and algorithms along with Python and Pandas, helps prepare learners to handle the types of data that data analysts work with. This course emphasizes data handling techniques that are critical in the work of a Data Analyst. The material on data analysis, visualization, and understanding of regression, prediction, and classification are useful for anyone working in the role of a Data Analyst.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Business intelligence analysts analyze business data to identify trends and provide insights to improve business decisions. This course provides a strong base in data handling, analysis, and visualization with Python and Pandas, which are helpful tools for business intelligence. Business intelligence analysts often make use of both regression and classification models, which are covered directly in this course. For anyone looking to work as a Business Intelligence Analyst, a course that focuses on these underlying techniques is a great choice.

See salaries and explore the career path for Business Intelligence Analyst

Statistician

Statisticians develop and apply statistical theories and methods for collecting, interpreting, and summarizing numerical data. While this role usually requires an advanced degree, this course may be useful by providing a foundation in data handling and statistical modeling techniques. This course covers many statistical methods such as regression, classification, and cluster analysis techniques. A statistician will make use of techniques covered in this course, such as statistical model building and assessment. This course may be helpful for one who seeks the role of statistician.

See salaries and explore the career path for Statistician

Research Scientist

Research scientists design and conduct experiments, analyze data, and publish findings. Although this role often requires an advanced degree, this course may be useful to those interested in building skills for quantitative research, especially within the data science domain. The course's emphasis on data handling with Python and Pandas, along with techniques for regression, classification, and cluster analysis, are all very relevant to research. A research scientist may make use of these techniques, which are covered in this course. This course may be useful for this type of role.

See salaries and explore the career path for Research Scientist

Quantitative Analyst

Quantitative analysts, often referred to as 'quants', develop and implement mathematical and statistical models for financial applications. This course, while not explicitly focused on finance, covers crucial techniques for mathematical modeling and data analysis. A quantitative analyst uses skills in regression, prediction, and classification, all of which are explored in the course material. The focus on practical data handling with Python and Pandas will also help a quantitative analyst with data management. This course may be helpful to someone who wishes to work as a Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Bioinformatician

Bioinformaticians handle biological data using computational methods. This role typically requires an advanced degree. This course may be useful for people interested in this field, given the course's coverage of data handling, regression, and classification techniques using Python and Pandas. A bioinformatician would be able to apply the techniques of this course to their work. This course may provide a foundation that is helpful for someone wanting to work as a Bioinformatician.

See salaries and explore the career path for Bioinformatician

Operations Research Analyst

Operations research analysts use analytical and mathematical techniques to improve business operations. They use optimization and decision-making techniques, which require mathematical and programming skills. The course’s coverage of Python, Pandas, and data handling directly supports the kind of data work that an operations research analyst would expect to perform, and the material on regression and prediction are particularly useful. This course may be useful for anyone wanting to work as an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial analysts evaluate financial data, make investment recommendations, and help organizations make informed financial decisions. This role requires strong analytical and data management skills, which a course like this can help develop. The course’s instruction on Python and Pandas for data handling, data analysis, and practical quantitative methods are especially helpful for a financial analyst. Anyone who takes a course like this will better understand statistical modeling and data, which will make them a more effective Financial Analyst.

See salaries and explore the career path for Financial Analyst

Marketing Analyst

Marketing analysts study the market dynamics, consumer behavior, and effectiveness of marketing campaigns. This role requires an ability to work with data and to make data-driven recommendations, and a course like this may be useful. The course provides an introduction to data handling with Pandas and Python, as well as techniques such as regression and classification, which may be useful in the work of a Marketing Analyst. This course may be helpful for someone interested in working as a Marketing Analyst.

See salaries and explore the career path for Marketing Analyst

Risk Analyst

Risk analysts identify and assess potential risks, both financial and non-financial, to help organizations mitigate these risks. This course may be useful because it introduces data analysis and modeling techniques. A risk analyst may use the skills they learn in this course to analyze historical data and build models to predict and manage risk. The techniques of regression, prediction, and classification covered in the course are all relevant to this work. This course may be helpful for anyone wanting to work as a Risk Analyst.

See salaries and explore the career path for Risk Analyst

Database Administrator

Database administrators maintain and manage computer databases. Data storage, access, and organization skills are needed for this role. This course covers data handling techniques using Python and Pandas, which may provide someone with a basic overview of how data is organized, stored, and prepared for analysis. This course may be useful for someone who wants to understand more about how data is structured and prepared in a database. A database administrator may benefit from the knowledge about data handling that this course provides.

See salaries and explore the career path for Database Administrator

Software Developer

Software developers write and maintain code for computer systems and applications. This course may be useful because it provides a deep dive into Python programming language, which is widely used in the software development space. Also, concepts of data handling that are covered in this course may be useful to software developers who work with data. This course may be useful for a software developer who needs to handle data in their work.

See salaries and explore the career path for Software Developer

Research Associate

Research associates typically assist senior researchers with experiments, data collection, and data analysis, often within an academic or scientific setting. While this role usually requires an advanced degree, this course may help those people new to research develop skills in data handling and analysis. The course's focus on Python, Pandas, and data handling, in addition to techniques for regression and classification, may be beneficial for research. This course may provide a foundation for a research associate who works with data.

See salaries and explore the career path for Research Associate

Data Science Methods and Algorithms [2025]

What's inside

Learning objectives

Syllabus

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Reviews summary

Comprehensive data science methods with python & pandas

Activities

Career center

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