Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. This course provide the key foundational skills any data scientist needs to prepare for a career in data science or further advanced learning in the field.
In this module, learners will the basics of data science such as data scientist’s tool set, programming language, data analysis, data cleaning and data visualization. It also covers the details about data scientist role and day-to-day job scope of this role.
This module provides a nontechnical overview of how data science functions in various sectors such as public sector, education management, healthcare system, banking and finance, sport management, media and entertainment, retail and commerce, gaming and sports, and non-profit organisation.
In this module, learners will learn the foundations of data science including big data concepts, tools, and techniques, gathering, and sorting data, working with databases, understanding structured and unstructured data types, applying statistical analysis, creating a story with data to communicate a complex idea and how to work within a data science life cycle (DSLC) – a methodology for cycling through questions, research, and reporting every two weeks.
In this module, learners will learn the overview of the field, covering the vocabulary, skills, tools, techniques of data science, roles of databases in data science, as well as the key feature and performance requirements for databases, systematic approach to the data understanding phase for predictive modelling and how to bring software engineering and data mining methodologies to data scientists and how to apply in data science by taking a simple business need.
In this module, learners will learn the tools, techniques, and tactical thinking behind data mining. It also covers data sources and types, the languages, software used in data mining (including R and Python), big data’s relationship to AI, social media, and the Internet of Things (IoT) and the best practices of data visualization.
In this module, learners will get hands-on experience with various programming methods.
In this part, learners will get the hands-on experience with Python. They will learn the techniques to clean, reformat, transform, and describe raw data, machine learning models and how to perform linear and logistic regression, use K-means and hierarchal clustering, identify relationships between variables, and use other machine learning tools.
In this part, learners will understand the most common language for database wrangling, SQL. It covers all the major features of SQL: creating tables; defining relationships; manipulating strings, numbers, and dates; using triggers to automate actions; extracting data with the relational database; and designing data models and optimizing queries in SQL.
In this part, learners will learn many flavors of the R programming language, including base R, tidy verse R, R Open from Microsoft, and Bioconductor R. It covers programming with R interactively and the command line, and introduces some helpful packages for working with SQL, 3D graphics, data, and clusters in R. It also shows why R is ideal for high volumes of data, different types of high-velocity data, high variety of data, how dates and times are stored and retrieved in base R and how to connect Tableau to R, and covers geocoding, running linear regression models and clustering.
This module will introduce the learners to the essentials of graphical visualization of data using Tableau, GitHub, Hadoop and Docker, Google Cloud.
In this module, learners will learn how to get started in their careers, navigated different roles, and how they continue to advance and master their skills. It also covers 12 common misconceptions within the field of data science, 15 data science mistakes and learn why the most promising data science insights fall flat without a compelling story.
In this module, learners will learn the nontechnical skills that can help them convert their first data science job into a successful, lifelong career. It also includes the five biggest career opportunities, leading industry-recognized certifications, and the most exciting emerging technologies. They will also learn how to use their data science and analytics expertise in a variety of ways—from writing books to delivering talks at conferences.
All students will receive a Certification of Completion by Asia Pacific Sales & Marketing Academy upon completing all online modules and passing all integrated quizzes.
APACSMA will issue a digital badge and eCertificate within 2 weeks of completion. You will receive a notification from firstname.lastname@example.org.
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