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via Udemy |
Go to Course: https://www.udemy.com/course/datascience_machine-learning-nlp-python-r-bigdata-pyspark/
Certainly! Here's a comprehensive review and recommendation for the Coursera course on Data Science with R, Python, and Spark: --- **Course Review: Data Science with R, Python, and Spark** In today’s data-driven world, a career in Data Science continues to be one of the most sought-after and lucrative professions. This Coursera course offers a comprehensive introduction to Data Science, equipping learners with essential skills in Python, R, and Spark, the most prominent tools and languages in the field. **Why I recommend this course:** - **Market-Relevant Content:** The course aligns with industry trends, emphasizing Python’s dominance in Data Science. It covers critical areas such as Machine Learning algorithms—including K-Means Clustering, Decision Trees, Random Forests, and Naive Bayes—applying these techniques through real-world scenarios across media, healthcare, social media, aviation, and HR sectors. - **Hands-On Learning:** The curriculum includes practical exercises on data extraction, wrangling, visualization, and analysis. Using popular libraries like Pandas, NumPy, Matplotlib, and Seaborn, students get the chance to perform end-to-end data projects, reinforcing their understanding. - **Broad Technical Scope:** Beyond Python, the course introduces key concepts in Statistics, Time Series, Text Mining, and an introduction to Deep Learning. This breadth ensures learners develop a well-rounded skill set. - **Industry-Oriented:** The implementation of real-life use cases helps students see how their skills apply outside the academic realm, making the learning highly relevant to current industry needs. - **Expert Instruction:** The course offers clear explanations of complex topics such as data inference, statistical measures, and machine learning categories, making it suitable for beginners and those looking to deepen their knowledge. **What I appreciated most:** - The focus on practical skills that can be immediately applied, such as web scraping using Scrapy and Beautiful Soup. - The introduction to Big Data tools like Hadoop and Spark, which are vital in handling large datasets. - The detailed coverage of the entire data science lifecycle from data extraction to visualization. **My Recommendation:** Whether you're a beginner eager to enter the data science field or a professional seeking to upgrade your skills, this course serves as an excellent gateway. It offers a perfect balance of theoretical foundations and hands-on projects, ensuring you gain not only knowledge but also confidence to tackle real-world data problems. **Final Verdict:** This course is highly recommended for aspiring Data Scientists aiming to master Python and R for analytical tasks, as well as those interested in Big Data platforms like Spark. It provides a solid foundation in both programming and statistical analysis, preparing you for the evolving demands of the data science industry. --- **Enroll today and start transforming data into actionable insights!**
Data Scientist is amongst the trendiest jobs, Glassdoor ranked it as the #1 Best Job in America in 2018 for the third year in a row, and it still holds its #1 Best Job position. Python is now the top programming language used in Data Science, with Python and R at 2nd place. Data Science is a field where data is analyzed with an aim to generate meaningful information. Today, successful data professionals understand that they require much-advanced skills for analyzing large amounts of data. Rather than relying on traditional techniques for data analysis, data mining and programming skills, as well as various tools and algorithms, are used. While there are many languages that can perform this job, Python has become the most preferred among Data Scientists.Today, the popularity of Python for Data Science is at its peak. Researchers and developers are using it for all sorts of functionality, from cleaning data and Training models to developing advanced AI and Machine Learning software. As per Statista, Python is LinkedIn's most wanted Data Science skill in the United States.Data Science with R, Python and Spark Training lets you gain expertise in Machine Learning Algorithms like K-MeansClustering, Decision Trees, Random Forest, and Naive Bayes using R, Python and Spark. Data Science Trainingencompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introductionto Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases onMedia, Healthcare, Social Media, Aviation and HR.CurriculumIntroduction to Data ScienceLearning Objectives - Get an introduction to Data Science in this module and see how Data Sciencehelps to analyze large and unstructured data with different tools.Topics:What is Data Science? What does Data Science involve?Era of Data Science Business Intelligence vs Data ScienceLife cycle of Data Science Tools of Data ScienceIntroduction to Big Data and Hadoop Introduction to RIntroduction to Spark Introduction to Machine LearningStatistical InferenceLearning Objectives - In this module, you will learn about different statistical techniques andterminologies used in data analysis.Topics:What is Statistical Inference? Terminologies of StatisticsMeasures of Centers Measures of SpreadProbability Normal DistributionBinary DistributionData Extraction, Wrangling and ExplorationLearning Objectives - Discuss the different sources available to extract data, arrange the data instructured form, analyze the data, and represent the data in a graphical format.Topics:Data Analysis Pipeline What is Data ExtractionTypes of Data Raw and Processed DataData Wrangling Exploratory Data AnalysisVisualization of DataIntroduction to Machine LearningLearning Objectives - Get an introduction to Machine Learning as part of this module. You willdiscuss the various categories of Machine Learning and implement Supervised Learning Algorithms.Topics:What is Machine Learning? Machine Learning Use-CasesMachine Learning Process Flow Machine Learning CategoriesSupervised Learning algorithm: LinearRegression and Logistic Regression• Define Data Science and its various stages• Implement Data Science development methodology in business scenarios• Identify areas of applications of Data Science. • Understand the fundamental concepts of Python• Use various Data Structures of Python. • Perform operations on arrays using NumPy library• Perform data manipulation using the Pandas library• Visualize data and obtain insights from data using the Matplotlib and Seaborn library• Apply Scrapy and Beautiful Soup to scrap data from websites• Perform end to end Case study on data extraction, manipulation, visualization and analysis using Python