With the ever-growing importance of big data to all business,
the role of data scientists has never been more relevant.
Data scientists transform legacy business processes
to increase customer satisfaction and
help organizations evolve from being data aware to data driven.
Do you want to gather quality data, organize it to scale, and
easily visualize that data to make smart and informed business decisions?
Skillsoft Aspire Learning Journeys are designed to move people
forward within their organizations.
These intentionally designed courses start by providing novice software or
database programmers, who may be familiar with basic statistics but
are still swimming in various data sources, with foundational knowledge on
data architecture and statistical analysis to become true data analysts.
And skilled data analysts can start working on their data wrangling skills
by learning different methods of gathering, filtering, modifying, and
managing quality data inputs and outputs.
From there, data wranglers may move into a data ops role
that focuses more on deploying data throughout their organization,
and then eventually, aspire to become a data scientist who brings together
all the insights and knowledge acquired throughout their journey
to ready their business for the future.
Let's take a look at what your Data Analyst to Data Scientist journey
will entail.
The Data Analyst track is the first step in the journey towards
becoming a data scientist.
Data analysts are typically working with legacy systems,
dealing with mixed data sources that are located in siloed data, and
using Excel to do things it was never intended to do.
Because of these factors, their systems are brittle, don't scale well, and
can be insecure.
Your journey will begin with the key concepts of data architecture and
engineering.
It then transitions to programming in Python and R but
quickly progresses to using statistics for data science.
A variety of data science tools are employed, including Hadoop,
Spark, and HDFS,
as well as cloud-based data warehousing such as Amazon Web Services.
Data wranglers are typically focused on normalizing, cleaning, structuring,
automating, and transforming data in their organizations.
Track 2 of your journey focuses on data wrangling with Python and Pandas,
utilizing MongoDB with PyMongo for wrangling, and using R for cleaning data.
Learners will also find out how to implement data lakes for real-time data
management and unleash the transformational power of various data tools,
such as Apache Hive, Hadoop MapReduce, and Apache Spark.
The third step in becoming a data scientist is the data ops role,
which deals with deploying data throughout an organization.
They are actively involved in deploying real-time data pipelines,
setting up governance policies and protocols, building scaling and
backup strategies, and making sure that data is secure and
able to be integrated with other systems.
The training in this track progresses towards the final role of the data
scientist by covering the various data science tools that are utilized in
daily data operations, including PowerBI and ELK,
Kibana, Tableau, and Qlikview.
The fourth and last step to becoming a data scientist will bring together
the insights and knowledge acquired throughout your journey.
Data scientists at this stage can employ a verifiable scientific approach,
coupled with effective communication and
visualization skills, to provide valuable business insights.
This track caps off your training by dealing with raw data, using Tableau to
visualize data, and programming with Python and Seaborn and R.
It also delves into data research by focusing on exploration techniques and
statistical approaches as well as machine learning and
deep learning algorithms.
So where will your journey take you?
All of our Aspire Journeys for Technology and
Developer are created based on a proven approach to learning design,
which takes advantage of multimodal learning tools.
From videos to books, learners have access to a multitude of resources
that will orient them to key work functions and daily challenges.
Learners can also apply concepts and
manipulate data with hands-on practice labs, done virtually
using real equipment and applications, so that each learner can practice and
develop their skills without compromising their own company's systems.
At the end of each track within a journey, users must demonstrate
their newfound knowledge by passing a rigorous final exam.
Once a journey is fully completed, learners can continue their growth and
move on to another area of study.
Data scientists can move on to the Machine Learning Architect or
AI Developer journeys at their own pace,
enabling employees to continually learn, evolve, and develop their skills.