Foundations of Data Structures and Algorithms Specialization

 

In the realm of computer science, data structures and algorithms are the backbone of efficient programming and software development. They form the fundamental concepts that every aspiring software engineer, data scientist, and computer scientist must master to solve complex problems effectively. Coursera's "Data Structures and Algorithms" Specialization, offered by the University of Colorado Boulder, provides an in-depth journey into these essential topics, equipping learners with the skills needed to excel in the tech industry.

Why Data Structures and Algorithms Matter

Data structures and algorithms are the building blocks of all software applications. They enable programmers to handle data efficiently, optimize performance, and ensure that applications run smoothly. Understanding these concepts is crucial for:

• Problem Solving: Algorithms provide a set of instructions to solve specific problems, while data structures organize and store data for efficient access and modification.
• Efficiency: Efficient algorithms and data structures improve the speed and performance of applications, making them scalable and robust.
• Competitive Programming: Mastery of these topics is essential for acing technical interviews and excelling in competitive programming contests.
• Software Development: From simple applications to complex systems, every software development project relies on the principles of data structures and algorithms.

Course Overview

The Coursera Specialization on Data Structures and Algorithms consists of several courses designed to take learners from basic to advanced levels. Here's a glimpse of what each course offers:

1. Algorithmic Toolbox:

   • Introduction to the basic concepts of algorithms.
   • Study of algorithmic techniques like greedy algorithms, dynamic programming, and divide-and-conquer.
   • Practical problem-solving sessions to reinforce learning.

2. Data Structures:

   • Comprehensive coverage of fundamental data structures such as arrays, linked lists, stacks, queues, trees, and graphs.
   • Exploration of advanced data structures like heaps, hash tables, and balanced trees.
   • Hands-on exercises to implement and manipulate various data structures.

3. Algorithms on Graphs:

   • Detailed study of graph algorithms including breadth-first search (BFS), depth-first search (DFS), shortest paths, and minimum spanning trees.
   • Real-world applications of graph algorithms in networking, web search, and social networks.

4. Algorithms on Strings:

   • Techniques for string manipulation and pattern matching.
   • Algorithms for substring search, text compression, and sequence alignment.
   • Applications in bioinformatics, data compression, and text processing.

5. Advanced Algorithms and Complexity:

   • Exploration of advanced topics such as NP-completeness, approximation algorithms, and randomized algorithms.
   • Analysis of algorithmic complexity and performance optimization.

Key Features

• Expert Instruction: The courses are taught by experienced professors from the University of Colorado Boulder, ensuring high-quality instruction and guidance.
• Interactive Learning: Each course includes a mix of video lectures, quizzes, programming assignments, and peer-reviewed projects to enhance learning.
• Flexibility: Learners can progress at their own pace, making it convenient to balance studies with other commitments.
• Certification: Upon completion, participants receive a certificate that can be shared on LinkedIn and added to their resumes, showcasing their proficiency in data structures and algorithms.

Who Should Enroll? Foundations of Data Structures and Algorithms Specialization

This specialization is ideal for:

• Aspiring Programmers: Beginners looking to build a strong foundation in data structures and algorithms.
• Software Engineers: Professionals seeking to improve their problem-solving skills and prepare for technical interviews.
• Computer Science Students: Individuals aiming to deepen their understanding of core computer science concepts.
• Tech Enthusiasts: Anyone with a passion for technology and a desire to learn how to solve complex problems efficiently.

Conclusion

Mastering data structures and algorithms is a crucial step towards becoming a proficient software engineer and problem solver. Coursera's "Data Structures and Algorithms" Specialization offers a comprehensive and structured learning path to achieve this mastery. With expert instruction, interactive learning experiences, and the flexibility to learn at your own pace, this specialization is an invaluable resource for anyone looking to excel in the tech industry.

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Databases and SQL for Data Science with Python

 

If you're looking to break into the world of data science, mastering SQL is a crucial step. Coursera offers a comprehensive course titled "SQL for Data Science" that provides a solid foundation in SQL, tailored for aspiring data scientists.

Course Overview

The "SQL for Data Science" course on Coursera is designed to equip you with the essential SQL skills needed to handle and analyze data. It's ideal for beginners, requiring no prior experience in SQL or database management.

Key Features

• Foundational Skills: The course covers the basics of SQL, including writing queries, filtering, sorting, and aggregating data. You'll learn how to use SQL to extract valuable insights from large datasets.
• Hands-On Projects: Practical exercises and projects ensure that you apply what you learn in real-world scenarios. This hands-on approach helps reinforce your understanding and build confidence in your SQL skills.
• Professional Certificates: Upon completion, you receive a certificate from Coursera, which is highly regarded by employers. According to Coursera, 88% of employers believe that Professional Certificates strengthen a candidate’s job application​ (Coursera)​.

Benefits of Learning SQL

1. High Demand: SQL is a highly sought-after skill in the tech industry. Many data-related roles require proficiency in SQL, making it a valuable addition to your resume.
2. Versatility: SQL is used in various industries, including finance, healthcare, marketing, and more. This versatility ensures that your skills are applicable across multiple fields.
3. Career Advancement: Completing this course can enhance your employability and open up opportunities for roles such as data analyst, database administrator, and data scientist​ (Coursera)​​ 

Course Content

The course is structured into several modules, each focusing on different aspects of SQL:

• Introduction to SQL: Learn the basics of SQL, including syntax and key concepts.
• Data Management: Understand how to manage databases and perform essential operations like inserting, updating, and deleting data.
• Data Analysis: Gain skills in data analysis, including using functions, subqueries, and joins to manipulate and analyze data.
• Advanced Topics: Explore advanced SQL topics such as window functions, stored procedures, and performance optimization.

Why Choose Coursera?

Coursera's platform is known for its high-quality content delivered by industry experts and top universities. The "SQL for Data Science" course is no exception, providing:

• Flexible Learning: Study at your own pace with access to video lectures, readings, and quizzes.
• Interactive Learning: Engage with peers and instructors through discussion forums and group projects.
• Credible Certification: Earn a certificate from a globally recognized platform, boosting your credentials in the job market​ (Coursera)​.

If you're ready to enhance your data science skills with SQL, consider enrolling in the "SQL for Data Science" course on Coursera. It's a step towards mastering data manipulation and analysis, crucial for a successful career in data science.

Join Free: Exploring Coursera's SQL for Data Science Course

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Introduction to Network Automation

 

Build your subject-matter expertise

This course is part of the Network Automation Engineering Fundamentals Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Introduction to Network Automation

There are 3 modules in this course

The Network infrastructure industry has undergone a significant transformation in recent years, with an increasing need for automation due to factors such as a demand for faster and more reliable network deployments. Therefore, there is a growing need for network engineers skilled in automation and programmability.

This course is primarily intended for network engineers, systems engineers, network architects, and managers interested in learning the fundamentals of network automation.

By the end of the course, you will be able to:

- Articulate the role network automation and programmability plays in the context of end-to-end network management and operations.

- Interpret Python scripts with fundamental programming constructs built for network automation use cases.

To be successful in this course, you should be proficient in fundamental network routing & switching technologies, understand the basics of Python programming (3-6 mos exp.), and have some familiarity with Linux.

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Programmation pour tous (mise en route de Python)

 

Introduction

Si vous êtes débutant en programmation et que vous souhaitez apprendre Python, le cours "Programming for Everybody (Getting Started with Python)" de l'Université du Michigan sur Coursera est parfait pour vous. Ce cours, dispensé en français, couvre les bases essentielles de la programmation en Python et est conçu pour ceux qui n'ont aucune expérience préalable en codage.

Contenu du Cours

Le cours comprend :

• Installation de Python : Guide étape par étape pour installer Python sur votre ordinateur.
• Écriture de votre premier programme : Introduction aux bases de la syntaxe Python.
• Utilisation des variables et des fonctions : Apprenez à manipuler les données et à structurer votre code.
• Boucles et instructions conditionnelles : Découvrez comment contrôler le flux de votre programme.

Structure du Cours

Le cours est structuré de manière à faciliter l'apprentissage avec des vidéos explicatives, des lectures, des quiz interactifs et des devoirs pratiques. Il est conçu pour être suivi à votre propre rythme, ce qui vous permet de l'adapter à votre emploi du temps.

Pourquoi Apprendre Python?

Python est un langage de programmation polyvalent et très populaire. Il est utilisé dans divers domaines tels que le développement web, l'analyse de données, l'intelligence artificielle, et bien plus encore. En apprenant Python, vous ouvrez la porte à de nombreuses opportunités professionnelles.

Certification

À la fin du cours, vous aurez la possibilité d'obtenir un certificat partageable qui peut enrichir votre CV et démontrer vos compétences en programmation Python.

Conclusion

Le cours "Programming for Everybody (Getting Started with Python)" est une excellente opportunité pour débuter en programmation. Il offre une introduction complète et accessible à Python, avec le soutien d'une institution prestigieuse comme l'Université du Michigan.

Pour plus d'informations et pour vous inscrire, visitez la page du cours sur Coursera. Bon apprentissage !

rejoindre gratuitement: Programmation pour tous (mise en route de Python)

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Natural Language Processing Specialization

 

What you'll learn

Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words.

Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.

Use recurrent neural networks, LSTMs, GRUs & Siamese networks in Trax for sentiment analysis, text generation & named entity recognition.

Use encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, build chatbots & question-answering.

Join Free: Natural Language Processing Specialization

Specialization - 4 course series

Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. 

This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.

By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an 

AI-powered future

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. 

Younes Bensouda Mourri

 is an Instructor of AI at Stanford University who also helped build the 

Deep Learning Specialization

Łukasz Kaiser

 is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper. 

Applied Learning Project

This Specialization will equip you with machine learning basics and state-of-the-art deep learning techniques needed to build cutting-edge NLP systems:

• Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, translate words, and use locality-sensitive hashing to approximate nearest neighbors.

• Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.

• Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions. 

• Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering, and to build chatbots. Learn T5, BERT, transformer, reformer, and more with 🤗  Transformers!

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Practical Time Series Analysis

 

There are 6 modules in this course

Welcome to Practical Time Series Analysis!

Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training.  This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. 

In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more.  We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future.

Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn.

You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself!

Join Free: Practical Time Series Analysis

Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!

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An Intuitive Introduction to Probability

 

There are 5 modules in this course

This course will provide you with a basic, intuitive and practical introduction into Probability Theory. You will be able to learn how to apply Probability Theory in different scenarios and you will earn a "toolbox" of methods to deal with uncertainty in your daily life. 

The course is split in 5 modules. In each module you will first have an easy introduction into the topic, which will serve as a basis to further develop your knowledge about the topic and acquire the "tools" to deal with uncertainty. Additionally, you will have the opportunity to complete 5 exercise sessions to reflect about the content learned in each module and start applying your earned knowledge right away. 

The topics covered are: "Probability", "Conditional Probability", "Applications", "Random Variables", and "Normal Distribution".

Join Free : An Intuitive Introduction to Probability

You will see how the modules are taught in a lively way, focusing on having an entertaining and useful learning experience! We are looking forward to see you online!

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Fractal Data Science Professional Certificate

 

What you'll learn

 Apply structured problem-solving techniques to dissect and address complex data-related challenges encountered in real-world scenarios.   

Utilize SQL proficiency to retrieve, manipulate data and employ data visualization skills using Power BI to communicate insights.

Apply Python expertise for data manipulation, analysis and implement machine learning algorithms to create predictive models for applications.

Create compelling data stories to influence your audience and master the art of critically analyzing data while making decisions and recommendations.

Join Free: Fractal Data Science Professional Certificate

Professional Certificate - 8 course series

Data science is projected to create 11.5 1 million global job openings by 2026 and offers many of the remote 2 job opportunities in the industry.

Prepare for a new career in this high-demand field with a Professional Certificate from Fractal Analytics. Whether you're a recent graduate seeking a rewarding career shift or a professional aiming to upskill, this program will equip you with the essential skills demanded by the industry.

This curriculum is designed with a problem-solving approach at the center to equip and enable you with the skills, required to solve data science problems, instead of just focusing on the tools and applications.

Through hands-on courses you'll master Python programming, harness the power of machine learning, cultivate expertise in data manipulation, and build understanding of cognitive factors affecting decisions. You will also learn the direct application of tools like SQL, PowerBI, and Python to real-world scenarios.

Upon completion, you will earn a Professional Certificate, which will help to make your profile standout in your career journey.

Fractal Data Science Professional Certificate is one of the preferred qualifications for entry-level data science jobs at Fractal. Complete this certificate to make your profile standout from other candidates while applying for job openings at Fractal.

Applied Learning Project

Learners will be able to apply structured problem-solving techniques to dissect and address complex data-related challenges encountered in real-world scenarios and utilize SQL proficiency to retrieve and manipulate data and employ data visualization skills using Power BI to communicate insights. Becoming experts at Python programming to manipulate and analyze data. Learners will implement machine learning algorithms to create predictive models for diverse applications. And create compelling data stories to influence and inform your audience and master the art of critically analyzing data while making decisions and recommendations.

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CertNexus Certified Data Science Practitioner Professional Certificate

 

Advance your career with in-demand skills

Receive professional-level training from CertNexus

Demonstrate your technical proficiency

Earn an employer-recognized certificate from CertNexus

Prepare for an industry certification exam

Join Free: CertNexus Certified Data Science Practitioner Professional Certificate

Professional Certificate - 5 course series

The field of Data Science has topped the Linked In Emerging Jobs list for the last 3 years with a projected growth of 28% annually and the World Economic Forum lists Data Analytics and Scientists as the top emerging job for 2022. 

Data can reveal insights and inform business—by guiding decisions and influencing day-to-day operations. This specialization will teach learners how to analyze, understand, manipulate, and present data within an effective and repeatable process framework and will enable you to bring value to the business by putting data science concepts into practice. 

This course is designed for business professionals that want to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.

Certified Data Science Practitioner (CDSP)  will prepare learners for the CertNexus CDSP certification exam. 

To complete your journey to the CDSP Certification

Complete the Coursera Certified Data Science Practitioner Professional Certificate.

Review the CDSP Exam Blueprint

.

Purchase your CDSP Exam Voucher

Register for your CDSP Exam.

Applied Learning Project

At the conclusion of each course, learners will have the opportunity to complete a project which can be added to their portfolio of work.  Projects include: 

Address a Business Issue with Data Science 

Extract, Transform, and Load Data

Data Analysis

Training a Machine Learning Model

Presenting a Data Science Project

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IBM Data Engineering Professional Certificate

 

What you'll learn

Master the most up-to-date practical skills and knowledge data engineers use in their daily roles

Learn to create, design, & manage relational databases & apply database administration (DBA) concepts to RDBMSs such as MySQL, PostgreSQL, & IBM Db2 

Develop working knowledge of NoSQL & Big Data using MongoDB, Cassandra, Cloudant, Hadoop, Apache Spark, Spark SQL, Spark ML, and Spark Streaming 

Implement ETL & Data Pipelines with Bash, Airflow & Kafka; architect, populate, deploy Data Warehouses; create BI reports & interactive dashboards 

Join Free: IBM Data Engineering Professional Certificate

Professional Certificate - 13 course series

Prepare for a career in the high-growth field of data engineering. In this program, you’ll learn in-demand skills like Python, SQL, and Databases to get job-ready in less than 5 months.

Data engineering is building systems to gather data, process and organize raw data into usable information, and manage data. The work data engineers do provides the foundational information that data scientists and business intelligence (BI) analysts use to make recommendations and decisions.

This program will teach you the foundational data engineering skills employers are seeking for entry level data engineering roles, including Python, one of the most widely used programming languages. You’ll also master SQL, RDBMS, ETL, Data Warehousing, NoSQL, Big Data, and Spark with hands-on labs and projects.

You’ll learn to use Python programming language and Linux/UNIX shell scripts to extract, transform and load (ETL) data. You’ll also work with Relational Databases (RDBMS) and query data using SQL statements and use NoSQL databases as well as unstructured data. 

When you complete the full program, you’ll have a portfolio of projects and a Professional Certificate from IBM to showcase your expertise. You’ll also earn an IBM Digital badge and will gain access to career resources to help you in your job search, including mock interviews and resume support. 

This program is ACE® recommended—when you complete, you can earn up to 12 college credits.

Applied Learning Project

Throughout this Professional Certificate, you will complete hands-on labs and projects to help you gain practical experience with Python, SQL, relational databases, NoSQL databases, Apache Spark, building data pipelines, managing databases, and working with data warehouses.

Design a relational database to help a coffee franchise improve operations.

Use SQL to query census, crime, and school demographic data sets.

Write a Bash shell script on Linux that backups changed files.

Set up, test, and optimize a data platform that contains MySQL, PostgreSQL, and IBM Db2 databases.

Analyze road traffic data to perform ETL and create a pipeline using Airflow and Kafka.

Design and implement a data warehouse for a solid-waste management company.

Move, query, and analyze data in MongoDB, Cassandra, and Cloudant NoSQL databases.

Train a machine learning model by creating an Apache Spark application.

This program is FIBAA recommended—when you complete, you can earn up to 8 ECTS credits.

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Data Analysis and Visualization Foundations Specialization

 

What you'll learn

Describe the data ecosystem, tasks a Data Analyst performs, as well as skills and tools required for successful data analysis

Explain basic functionality of spreadsheets and utilize Excel to perform a variety of data analysis tasks like data wrangling and data mining

List various types of charts and plots and create them in Excel as well as work with Cognos Analytics to generate interactive dashboards

Join Free: Data Analysis and Visualization Foundations Specialization

Specialization - 4 course series

Deriving insights from data and communicating findings has become an increasingly important part of virtually every profession. This Specialization prepares you for this data-driven transformation by teaching you the core principles of data analysis and visualization and by giving you the tools and hands-on practice to communicate the results of your data discoveries effectively.  

You will be introduced to the modern data ecosystem. You will learn the skills required to successfully start data analysis tasks by becoming familiar with spreadsheets like Excel. You will examine different data sets, load them into the spreadsheet, and employ techniques like summarization, sorting, filtering, & creating pivot tables.

Creating stunning visualizations is a critical part of communicating your data analysis results. You will use Excel spreadsheets to create the many different types of data visualizations such as line plots, bar charts, pie charts. You will also create advanced visualizations such as treemaps, scatter charts & map charts. You will then build interactive dashboards. 

This Specialization is designed for learners interested in starting a career in the field of Data or Business Analytics, as well as those in other professions, who need basic data analysis and visualization skills to supplement their primary job tasks.

This program is ACE® recommended—when you complete, you can earn up to 9 college credits.  

Applied Learning Project

Build your data analytics portfolio as you gain practical experience from producing artifacts in the interactive labs and projects throughout this program. Each course has a culminating project to apply your newfound skills:

In the first course, create visualizations to detect fraud by analyzing credit card data.

In the second course, import, clean, and analyze fleet vehicle inventory with Excel pivot tables.

In the third course, use car sales key performance indicator (KPI) data to create an interactive dashboard with stunning visualizations using Excel and IBM Cognos Analytics.

Only a modern web browser is required to complete these practical exercises and projects — no need to download or install anything on your device.

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Introduction to Data Science Specialization

 

What you'll learn

Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists  

Gain hands-on familiarity with common data science tools including JupyterLab, R Studio, GitHub and Watson Studio 

Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems

Write SQL statements and query Cloud databases using Python from Jupyter notebooks

Join Free: Introduction to Data Science Specialization

Specialization - 4 course series

Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field.  

This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals.

You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets.

In addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in data science foundations.

This Specialization can also be applied toward the 

IBM Data Science Professional Certificate. 

Applied Learning Project

All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets and tools like Jupyter, GitHub, and R Studio. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:

Creating and sharing a Jupyter Notebook containing code blocks and markdown

Devising a problem that can be solved by applying the data science methodology and explain how to apply each stage of the methodology to solve it

Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools

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Data Analysis with Python

 

What you'll learn

Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Join Free: Data Analysis with Python

There are 6 modules in this course

Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.  

Topics covered include:  

- collecting and importing data 

- cleaning, preparing & formatting data 

- data frame manipulation 

- summarizing data 

- building machine learning regression models 

- model refinement 

- creating data pipelines 

You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them.  

In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions.  

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Get Started with Python by Google

 

What you'll learn

Explain how Python is used by data professionals 

Explore basic Python building blocks, including syntax and semantics

Understand loops, control statements, and string manipulation

Use data structures to store and organize data 

Join Free : Get Started with Python

There are 5 modules in this course

This is the second of seven courses in the Google Advanced Data Analytics Certificate. The Python programming language is a powerful tool for data analysis. In this course, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You'll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures. 

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. 

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.    

By the end of this course, you will:

-Define what a programming language is and why Python is used by data scientists

-Create Python scripts to display data and perform operations

-Control the flow of programs using conditions and functions

-Utilize different types of loops when performing repeated operations

-Identify data types such as integers, floats, strings, and booleans

-Manipulate data structures such as , lists, tuples, dictionaries, and sets

-Import and use Python libraries such as NumPy and pandas

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Probabilistic Graphical Models 1: Representation

 

Build your subject-matter expertise

This course is part of the Probabilistic Graphical Models Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free: Probabilistic Graphical Models 1: Representation

There are 7 modules in this course

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. 

This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.

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Evaluations of AI Applications in Healthcare

 

What you'll learn

Principles and practical considerations for integrating AI into clinical workflows

Best practices of AI applications to promote fair and equitable healthcare solutions

Challenges of regulation of AI applications and which components of a model can be regulated

What standard evaluation metrics do and do not provide

Join Free: Evaluations of AI Applications in Healthcare

There are 7 modules in this course

With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

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IBM Data Analytics with Excel and R Professional Certificate

 

What you'll learn

Master the most up-to-date practical skills and knowledge data analysts use in their daily roles

Learn how to perform data analysis, including data preparation, statistical analysis, and predictive modeling using R, R Studio, and Jupyter

Utilize Excel spreadsheets to perform a variety of data analysis tasks like data wrangling, using pivot tables, data mining, & creating charts

Communicate your data findings using various data visualization techniques including, charts, plots & interactive dashboards with Cognos and R Shiny

Join Free: IBM Data Analytics with Excel and R Professional Certificate

Professional Certificate - 9 course series

Prepare for the in-demand field of data analytics. In this program, you’ll learn high valued skills like Excel, Cognos Analytics, and R programming language to get job-ready in less than 3 months.

Data analytics is a strategy-based science where data is analyzed to find trends, answer questions, shape business processes, and aid decision-making. This Professional Certificate focuses on data analysis using Microsoft Excel and R programming language. If you’re interested in using Python, please explore the IBM Data Analyst PC. 

This program will teach you the foundational data skills employers are seeking for entry level data analytics roles and will provide a portfolio of projects and a Professional Certificate from IBM to showcase your expertise to potential employers.

You’ll learn the latest skills and tools used by professional data analysts and upon successful completion of this program, you will be able to work with Excel spreadsheets, Jupyter Notebooks, and R Studio to analyze data and create visualizations. You will also use the R programming language to complete the entire data analysis process,  including data preparation, statistical analysis, data visualization, predictive modeling and creating interactive dashboards. Lastly, you’ll learn how to communicate your data findings and prepare a summary report.

This program is ACE® and FIBAA recommended—when you complete, you can earn up to 15 college credits and 4 ECTS credits.

Applied Learning Project

You will complete hands-on labs to build your portfolio and  gain practical experience with Excel, Cognos Analytics, SQL, and the R programing language and related libraries for data science, including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.

Projects include:

Analyzing fleet vehicle inventory data using pivot tables.

Using key performance indicator (KPI) data from car sales to create an interactive dashboard.

Identifying patterns in countries’ COVID-19 testing data rates using R.

Using SQL with the RODBC R package to analyze foreign grain markets.

Creating linear and polynomial regression models and comparing them with weather station data to predict precipitation.

Using the R Shiny package to create a dashboard that examines trends in census data.

Using hypothesis testing and predictive modeling skills to build an interactive dashboard with the R Shiny package and a dynamic Leaflet map widget to investigate how weather affects bike-sharing demand.

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Predict Sales Revenue with scikit-learn

 

What you'll learn

Build simple linear regression models in Python

Apply scikit-learn and statsmodels to regression problems

Employ explorartory data analysis (EDA) with seaborn and pandas

Explain linear regression to both technical and non-technical audiences

Join Free: Predict Sales Revenue with scikit-learn

About this Guided Project

In this 2-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the scikit-learn module for calculating the linear regression, while using pandas for data management, and seaborn for plotting. You will be working with the very popular Advertising data set to predict sales revenue based on advertising spending through mediums such as TV, radio, and newspaper. 

By the end of this course, you will be able to:

- Explain the core ideas of linear regression to technical and non-technical audiences

- Build a simple linear regression model in Python with scikit-learn

- Employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas

- Evaluate a simple linear regression model using appropriate metrics

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Jupyter and Python 3.7 with all the necessary libraries pre-installed.

Notes:

- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.

- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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Generative AI: Enhance your Data Analytics Career

 

What you'll learn

Describe how you can use Generative AI tools and techniques in the context of data analytics across industries

Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools

Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights 

 Analyze the ethical considerations and challenges associated with using Generative AI in data analytics

Join Free: Generative AI: Enhance your Data Analytics Career

There are 3 modules in this course

This comprehensive course unravels the potential of generative AI in data analytics. The course will provide an in-depth knowledge of the fundamental concepts, models, tools, and generative AI applications regarding the data analytics landscape. 

In this course, you will examine real-world applications and use generative AI to gain data insights using techniques such as prompts, visualization, storytelling, querying and so on. In addition, you will understand the ethical implications, considerations, and challenges of using generative AI in data analytics across different industries.

You will acquire practical experience through hands-on labs where you will leverage generative AI models and tools such as ChatGPT, ChatCSV, Mostly.AI, SQLthroughAI and more.

Finally, you will apply the concepts learned throughout the course to a data analytics project. Also, you will have an opportunity to test your knowledge with practice and graded quizzes and earn a certificate. 

This course is suitable for both practicing data analysts as well as learners aspiring to start a career in data analytics. It requires some basic knowledge of data analytics, prompt engineering, Python programming and generative artificial intelligence.

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Data Analyst Career Guide and Interview Preparation

 

What you'll learn

Describe the role of a data analyst and some career path options as well as the prospective opportunities in the field.

Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.

Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.

Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.

Join Free: Data Analyst Career Guide and Interview Preparation

There are 4 modules in this course

Data analytics professionals are in high demand around the world, and the trend shows no sign of slowing. There are lots of great jobs available, but lots of great candidates too. How can you get the edge in such a competitive field?

This course will prepare you to enter the job market as a great candidate for a data analyst position. It provides practical techniques for creating essential job-seeking materials such as a resume and a portfolio, as well as auxiliary tools like a cover letter and an elevator pitch. You will learn how to find and assess prospective job positions, apply to them, and lay the groundwork for interviewing. 

The course doesn’t stop there, however. You will also get inside tips and steps you can use to perform professionally and effectively at interviews. You will learn how to approach a take-home challenges and get to practice completing them. Additionally, it provides information about the regular functions and tasks of data analysts, as well as the opportunities of the profession and some options for career development.

You will get guidance from a number of experts in the data industry through the course. They will discuss their own career paths and talk about what they have learned about networking, interviewing, solving coding problems, and fielding other questions you may encounter as a candidate. Let seasoned data analysis professionals share their experience to help you get ahead and land the job you want.

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