Data Science Bootcamp for Beginners: Learn the Essentials of Big Data Analytics

Foundations of Data Science

It’s critical to lay a strong data science foundation before delving into big data analytics. Data collection, cleansing, analysis, and visualization are just a few of the phases that make up the data science process. The Data Science Bootcamp for Beginners will teach participants about popular data science tools and methods, including Python, R, and SQL. In order to work with huge data sets, they will also learn about data types and data structures.

Data Science Bootcamp for Beginners

Data Collection and Cleaning

Data collection is the process of acquiring data from diverse sources. Big data might have a significant amount of data, and gathering it can be difficult. The Data Science Bootcamp for Beginners will teach participants how to gather sizable data sets using methods like web scraping, APIs, and data feeds. Additionally, they will become familiar with tools for handling sizable data sets including Apache Hadoop and Apache Spark.

Data cleaning – Finding and fixing data mistakes is the process of data cleansing. Data cleaning might take a long time when dealing with huge data sets, yet it is necessary to guarantee the analysis’s accuracy. Best practices and technologies for data cleansing, including OpenRefine and Trifacta, will be covered during the bootcamp. They will get knowledge on how to locate missing data, eliminate duplicates, and fix data problems.

Data Wrangling is the process of transforming and reshaping data to prepare it for analysis. This step is essential when working with big data, as the data can be unstructured and require significant preparation before analysis. Participants will learn about data wrangling techniques, such as data normalization and transformation. They’ll also get hands-on experience with data wrangling tools, such as Python’s Pandas library.

Data Integration is the process of combining data from multiple sources into a single data set for analysis. Participants will learn about data integration techniques, such as merging, joining, and concatenation. They’ll also get hands-on experience with data integration tools, such as Apache Pig and Apache Hive.

Data Quality Assessment

Data quality assessment is the process of evaluating the quality of the data to ensure that it’s fit for analysis. Participants will learn about data quality assessment techniques, such as outlier detection, data profiling, and data validation. They’ll also get hands-on experience with data quality assessment tools, such as Talend Data Quality and DataWrangler.

Big Data Analytics

Participants will dive into big data analytics after laying the groundwork. They will gain knowledge of the ideas and methods employed in big data analytics, including data exploration and visualization. Participants will learn how to organize big data into manageable chunks for analysis because it can be overwhelming. Additionally, they will receive a review of the algorithms and methods used in data analysis, including decision trees, clustering, and regression analysis.

Data exploration is the process of finding patterns, connections, and trends in the data. Data exploration can be difficult when working with large data since the amount of data is often overwhelming. The Data Science Bootcamp for Beginners teaches participants how to explore large data using tools like data visualization and statistical analysis. Additionally, they will learn about data exploration tools like Tableau and Power BI.

Data visualization is the process of representing data graphically to communicate insights and findings. Participants will learn about data visualization techniques, such as scatter plots, histograms, and heatmaps. They’ll also get hands-on experience with data visualization tools, such as Matplotlib and Seaborn.

Descriptive analytics is the process of summarizing and describing the data to gain insights into past performance. Participants will learn about descriptive analytics techniques, such as mean, median, and mode. They’ll also learn about tools for descriptive analytics, such as Apache Pig and Apache Hive.

Predictive analytics is the practice of determining the likelihood of future outcomes based on historical data by employing data, statistical algorithms, and machine learning approaches. An overview of predictive analytics methods, including decision trees, clustering, and regression analysis, will be given to participants. Additionally, they will learn about tools for predictive analytics like TensorFlow and Scikit-Learn.

Prescriptive analytics is the process of using data, mathematical algorithms, and computational models to identify the best course of action to take in a given situation. Participants will get an overview of prescriptive analytics techniques, such as optimization and simulation. They’ll also learn about tools for prescriptive analytics, such as Gurobi and IBM CPLEX.

Machine Learning Basics

Machine learning is a critical component of data science, and participants will get an introduction to the basics of supervised and unsupervised learning. They’ll learn about model selection and evaluation and get an overview of popular machine learning algorithms, such as linear regression, logistic regression, k-nearest neighbors, and support vector machines.

Data Science Project

Without a data science project, no data science bootcamp is complete. at order to put the concepts they have learned at the bootcamp into practice, participants will cooperate. To address a real-world issue, they will gather, purify, examine, and visualize a massive data collection while also creating a machine learning model. Participants will gain practical experience with the data science methodology through this project, preparing them for further data science tasks.

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