Develop yourself from Data Analyst to Data Scientist (incl. guidance)

Data engineering is the area of data science that focuses on practical applications of data collection and analysis. In this course, you will explore distributed systems, batch vs. in-memory processing, NoSQL uses, and the various tools available for data management/big data and the ETL process.

Discover how to apply regression

Explore the two most basic types of descriptive statistics, measures of central tendency and dispersion. Examine the most common measures of each type, as well as their strengths and weaknesses.

The goal of all modeling is generalizing as well as possible from a sample to the population as a whole. Explore the first step in this process, obtaining a representative sample from which meaningful generalizable insights can be obtained.

Inferential statistics go beyond merely describing a dataset and seek to posit and prove or disprove the existence of relationships within the data. Explore hypothesis testing, which finds wide applications in data science.

Apache Hadoop is a collection of open-source software utilities that facilitates solving data science problems. In this course, you will explore the theory behind big data analysis using Hadoop and how MapReduce enables the parallel processing of large datasets distributed on a cluster of machines.

Getting Started with Hadoop: Developing a Basic MapReduce Application

HDFS is the file system which enables the parallel processing of big data in distributed cluster. Explore the concepts of analyzing large datasets and explore how Hadoop and HDFS make this process very efficient.

Discover how to set up a Hadoop Cluster on the cloud and explore the bundled web apps - the YARN Cluster Manager app and the HDFS NameNode UI. Then use the hadoop fs and hdfs dfs shells to browse the Hadoop file system.

Explore the Hadoop file system using the HDFS dfs shell and perform basic file and directory-level operations. Transfer files between a local file system and HDFS and explore ways to create and delete files on HDFS.

HDFS is the file system which enables the parallel processing of big data in distributed cluster. When managing a data warehouse, not all users should be given free reign over all the datasets. Explore how file permissions can be viewed and configured in HDFS. The NameNode UI is used to monitor and explore HDFS.

• Traditional data warehousing is transitioning to be more

• cloud-based and this can be a key area that must be mastered for
• data science. In this course you will examine the organizational
• implications of data silos and explore how data lakes can help make
• data secure, discoverable, and queryable. Discover how data lakes
• can work with batch and streaming data.

• Traditional data warehousing is transitioning to be more

• cloud-based and this can be a key area that must be mastered for
• data science. In this course, you will discover how to build a data
• lake on the AWS cloud by storing data in S3 buckets and indexing
• this data using AWS Glue. Explore how to run crawlers to
• automatically crawl data in S3 to generate metadata tables in
• Glue.

• Traditional data warehousing is transitioning to be more

• cloud-based and this can be a key area that must be mastered for
• data science. In this course, you will discover how to configure
• Glue crawlers to work with different data stores on AWS. Examine
• how to visualize the data stored in the data lake with AWS
• QuickSight and how to perform ETL operations on the data using Glue
• scripts.

Discover how to perform data analysis using Anaconda Python, R, and related analytical libraries and tools.

• Practice performing data analysis tasks using Python by

• configuring VSCode, loading data from SQLite into Pandas, grouping
• data and using box plots. Then, test your skills by answering
• assessment questions after using Python to calculate frequency
• distribution, measures of center, and coefficient of
• dispersion.

Final Exam: Data Analyst will test your knowledge and application of the topics presented throughout the Data Analyst track of the Skillsoft Aspire Data Analyst to Data Scientist Journey.

Discover how to perform data transformations, data cleaning, and statistical aggregations using Pandas DataFrames.

Visualize and explore data in Pandas using popular chart types like the bar graph, histogram, pie chart, and box plot. Discover how to work with time series and string data in datasets.

Discover how to perform advanced grouping, aggregations, and filtering operations on DataFrames. Working with masks and indexes, cleaning duplicated data, and assigning columns as categorical to perform operations is also covered.

Explore the essential methods for wrangling data with R.

Explore the technology landscape and the tools used to implement data management. Discover how to use machine learning in data analytics and the capabilities of machine learning implementation in the cloud.

Explore machine learning solutions provided by AWS and Microsoft. Compare the prominent tools and frameworks that can be used to implement machine learning and deep learning.

Discover the essential methods for wrangling data with Trifacta.

To carry out data wrangling tasks you need to gather, filter, modify, and query data. Perform MongoDB actions related to data wrangling using Python with the PyMongo library.

To carry out data wrangling tasks you need to reshape, aggregate, and summarize data. Perform MongoDB actions related to data wrangling through the PyMongo library.

Apache Hive is one of the most

Hive simplifies working with large

• Apache Hive is one of the most popular data warehouses out in

• the market used for data science. It simplifies working with large
• datasets in files by representing them as tables. This allows them
• to be queried with a simple and intuitive query language. In this
• course you will explore how to create, load, and query Hive
• tables.

Hive allows analysis of big data by

In this course, you will explore the ways query executions can be optimized, including the powerful technique of partitioning datasets.

Explore how Hive query executions

Apache Hadoop is a collection of open-source software utilities that facilitates solving data science problems. Extracting only the meaningful information from a dataset can be painstaking, especially if it is very large. In this course, you will examine how Hadoop's MapReduce can be used to speed up this operation.

• Apache Hadoop is a collection of open-source software utilities

• that facilitates solving data science problems. Hadoop enables
• speedy analysis of large datasets by distributing them on a cluster
• and processing them in parallel. Explore the use of Combiners to
• make MapReduce applications more efficient by minimizing data
• transfers.

Apache Hadoop is a collection of open-source software utilities that facilitates solving data science problems. In this course, explore how MapReduce can be used to extract the five most expensive vehicles in a dataset and then how to build an inverted index for the words appearing in a set of text files.

Apache Spark is an open-source cluster-computing framework used for data science and it has become the defacto big data framework. In this course, you will explore how to analyze real datasets using the DataFrame API methods. Discover how to optimize operations using shared variables and combine data from multiple DataFrames using joins.

Discover how to design and implement data lakes in the cloud and on-premises using standard reference architectures and patterns that can help identify the proper data architecture.

Discover how to implement data lakes for real-time data management. Explore data ingestion, data processing, and data life-cycle management using AWS and other open-source ecosystem products.

Explore various types of data architecture and implementation of strategies using NoSQL, CAP theorem, and partitioning to improve performance.

Explore various types of data architecture and implementation of strategies using NoSQL, CAP theorem, and partitioning to improve performance.

Perform data wrangling tasks including using a Pandas DataFrame to convert multiple Excel sheets to separate JSON documents, extract a table from an HTML file, use mean substitution and convert dates within a DataFrame. Then, test your skills by answering assessment questions after using a Pandas DataFrame to convert a CSV document to a JSON document, replace missing values with a default value, split a column with a delimiter and combine two columns by concatenating text.

Final Exam: Data Wrangler will test your knowledge and application of the topics presented throughout the Data Wrangler track of the Skillsoft Aspire Data Analyst to Data Scientist Journey.

Discover the different uses of data

Explore the concept of dashboards and the best practices that can be adopted to build effective dashboards. How to implement dashboards and visualizations using PowerBI and ELK and the concepts of leaderboard and scorecards is also covered.

Explore the role played by dashboards in data exploration and deep analytics. Examine the essential patterns of dashboard design and how to implement appropriate dashboards using Kibana, Tableau, and Qlikview.

Discover how to implement cloud architecture for large scale applications, serverless computing, adequate storage, and analytical platforms using DevOps tools and cloud resources.

It's crucial that organizations remain compliant with their big data implementations. Examine compliance and its relationship with big data, as well as popular resources for developing compliance strategies

As organizations become more data science aware, it's critical to understand the role of governance in big data implementation. In this course you will examine governance and its relationship with big data, and how to plan and design a big data governance strategy.

Data sensitivity and security breaches are common in news media reports. Explore how a structured data access governance framework results in reducing the likelihood of data security breaches.

Before data can be sufficiently protected, its sensitivity must be known. Explore how data classification determines which security measure applies to varying classes of data.

Spark is an analytics engine built

Discover how to develop applications

Explore a theoretical foundation on the need for and the characteristics of scalable data architectures. Using data warehouses to store, process, and analyze big data is also covered.

Using a hands-on lab approach, explore how to use Amazon Redshift to set up and configure a data warehouse on the cloud. Discover how to interact with the Redshift service using both the console and the AWS CLI.

Explore the loading of data from an external source such as Amazon S3 into a Redshift cluster, as well as the configuration of snapshots and the resizing of clusters. Discover how to use Amazon QuickSight to visualize data.

Explore data pipelines and methods of processing them with and without ETL. Creating data pipelines using Apache Airflow is also covered.

Explore the concept of data pipelines, the processes and stages involved in building them, and the technologies like Tableau and AWS that can be used.

Discover how to implement data pipelines using Python Luigi, integrate Spark, and Tableau to manage data pipelines, use Dask arrays, and build data pipeline visualization with Python.

• To become proficient in data science, you have to understand

• edge computing. This is where data is processed near the source or
• at the edge of the network while in a typical cloud environment,
• data processing happens in a centralized data storage location. In
• this course you will exam the architecture of IoT solutions and the
• essential approaches of integrating data sources.

• To become proficient in data science, you have to understand

• edge computing. This is where data is processed near the source or
• at the edge of the network while in a typical cloud environment,
• data processing happens in a centralized data storage location. In
• this course you will explore the implementation of IoT on prominent
• cloud platforms like AWS and GCP. Discover how to work with IoT
• Device Simulator and generate data streams using MQTT.

Examine the security risks related to modern data capture and processing methods such as streaming analytics, the techniques and tools employed to mitigate security risks, and best practices related to securing big data.

Explore the concept of smart data and the associated life cycle and benefits afforded by smart data. Frameworks and algorithms that can help transition big data to smart data are also covered.

Explore the concepts of transactions, transaction management policies, and rollbacks. Discover how to implement transaction management and rollbacks using SQL Server.

Explore the differences between transaction management using NoSQL and MongoDB. Discover how to implement of change data capture in databases and NoSQL.

Perform data ops tasks with Python including working with row subsets, creating new columns with Regex, performing joins and spreading rows. Then, test your skills by answering assessment questions after working with field subsets and computed columns, and performing set operations and binding rows.

Final Exam: Data Ops will test your knowledge and application of the topics presented throughout the Data Ops track of the Skillsoft Aspire Data Analyst to Data Scientist Journey.

The four Vs of big data are a popular paradigm used to extract the meaning and value from massive datasets. Discover the four Vs, their purpose and uses, and how to extract value using the four Vs.

In order for an organization to be data science aware, it must evolve and become data driven. In this course, you will examine the meaning of a data driven organization and explore analytic maturity, data quality, missing data, duplicate data, truncated data, and data provenance.

To master data science it is important to take raw data and turn that into insights. In this course you will explore the concept of statistical analysis and implement data ingestion using various technologies including NiFi, Sqoop, and Wavefront.

To master data science it is important to take raw data and turn that into insights. In this course you will learn to apply and implement various essential data correction techniques, transformation rules, deductive correction techniques, and predictive modelling using critical data analytical approaches.

To become a data science expert, you must master the art of data visualization. In this course you will explore how to create and use real time dashboards with Tableau

Explore the concept of storytelling with data, the processes involved in storytelling and interpreting data contexts. We will also explore the prominent types of analysis, visualizations, and graphic tools that we can use for storytelling.

Explore how to select the most effective visuals for storytelling, eliminating clutters, and the best practices for story design. We will also learn to work with Tableau and PowerBI to facilitate storytelling with data.

Seaborn is a data visualization

Discover how to use the NumPy, Pandas, and SciPy libraries to perform various statistical summary operations on real datasets and how to visualize your datasets in the context of these summaries using Matplotlib.

Explore approaches to building and implementing visualizations, as well as plotting and graphing using Python libraries like Matplotlib, ggplot, bokeh, and Pygal.

Explore how to use R to create plots

and charts of data.

Discover how to build advanced charts using Python and Jupyter Notebook. Explore R and ggplot2 visualization capabilities and how to build charts and graphs with them.

Explore how Recommendation Engines can be created and used to provide recommendations for products and content.

Examine statistical and machine learning implementation methods and how to manage anomalies and improvise data for better data insights and accuracy.

Discover how to use machine learning methods and visualization tools to manage anomalies and improvise data for better data insights and accuracy.

Explore how different t-tests can be performed using the SciPy library to test hypotheses. How to calculate the skewness and kurtosis of data using SciPy and compute regressions using scikit-learn is also covered.

To master data science, you must learn the techniques around data research. In this course you will discover how to apply essential data research techniques, including JMP measurement, and how to valuate data using descriptive and inferential methods.

• To master data science, you must learn the techniques around

• data research. In this course you will discover how to use data
• exploration techniques to derive different data dimensions and
• derive value from the data. How to practically implement data
• exploration using R, Python, linear algebra, and plots is also
• covered.

Discover how to apply statistical algorithms like PDF, CDF, binomial distribution, and interval estimation for data research. How to implement visualizations to graphically represent the outcomes of data research is also covered.

Examine the fundamentals of machine learning and how Pandas ML can be used to build ML models. The workings of Support Vector Machines to perform classification of data is also covered.

Explore the fundamentals of regression and clustering and discover how to use a confusion matrix to evaluate classification models.

Classification, regression, and clustering are some of the most commonly used machine learning techniques and there are various algorithms available for these tasks. Explore their application in Pandas ML.

The imbalanced-learn library that integrates with Pandas ML offers several techniques to address the imbalance in datasets used for classification. Explore oversampling, undersampling, and a combination of these techniques.

Explore how to create RESTful OAuth APIs using Node.js.

Perform data visualization tasks with Python such as creating scatter plots, plotting linear regression, using logistic regression and creating decision tree. Then, test your skills by answering assessment questions after creating time-series graphs, resampling observations, creating histograms and using a grid pair.

Final Exam: Data Scientist will test your knowledge and application of the topics presented throughout the Data Scientist track of the Skillsoft Aspire Data Analyst to Data Scientist Journey.