ACC 157: DATA WAREHOUSING AND MANAGEMENT
Module #25
Name: _______________________________________________________________
Section: ____________ Schedule: ________________________________________
Lesson title: ANALYTICAL TOOLS
Lesson Objectives:
At the end of this module, I should be able to:
1. Search the best Data Analytic Tools
2. Determined the applicable tools in different platform
Class number: _______
Date: ________________
Materials:
Student Activity Sheets, Internet
Access, Computer/ Mobile Phones
References:
www.hackr.io
A. MAIN LESSON
1. Activity 2: Content Note’s
Data Analysis Tools
There are several data analysis tools available in the market, each with its own set of functions. The selection of tools
should always be based on the type of analysis performed, and the type of data worked.
Here is a list of a few compelling tools for Data Analysis:
1. Excel - It has a variety of compelling features, and with additional plugins installed, it can handle a massive
amount of data. So, if you have data that does not come near the significant data margin, then Excel can be a very
versatile tool for data analysis.
2. Tableau - It falls under the BI Tool category, made for the sole purpose of data analysis. The essence of Tableau
is the Pivot Table and Pivot Chart and works towards representing data in the most user-friendly way. It additionally
has a data cleaning feature along with brilliant analytical functions.
3. Power BI - It initially started as a plugin for Excel, but later on, detached from it to develop in one of the most data
analytics tools. It comes in three versions: Free, Pro, and Premium. Its PowerPivot and DAX language can
implement sophisticated advanced analytics similar to writing Excel formulas.
4. Fine Report - Fine Report comes with a straightforward drag and drops operation, which helps to design various
styles of reports and build a data decision analysis system. It can directly connect to all kinds of databases, and its
format is similar to that of Excel. Additionally, it also provides a variety of dashboard templates and several selfdeveloped visual plug-in libraries.
5. R & Python - These are programming languages which are very powerful and flexible. R is best at statistical
analysis, such as normal distribution, cluster classification algorithms, and regression analysis. It also performs
individual predictive analysis like customer behavior, his spend, items preferred by him based on his browsing
history, and more. It also involves concepts of machine learning and artificial intelligence.
6. SAS - It is a programming language for data analytics and data manipulation, which can easily access data from
any source. SAS has introduced a broad set of customer profiling products for web, social media, and marketing
analytics. It can predict their behaviors, manage, and optimize communications.
Now that you got an idea of different Analytical Tools use in Data Analysis. Let’s try a short activity to know how
much you understand the our short introduction to our lesson.
This document is the property of PHINMA EDUCATION
1
ACC 157: DATA WAREHOUSING AND MANAGEMENT
Module #25
Name: _______________________________________________________________
Section: ____________ Schedule: ________________________________________
Class number: _______
Date: ________________
2) Activity 3: Skill-building Activities.
There you go! I’m expecting that you learn something today, I am excited to hear your
understanding with our lesson for today, Answer the following question:
Try to answer it base on your understanding, better to construct your own sentences for
you to able practice your written communication skill.
Part 1: Search for the latest and best Data Analytic Tools used by different industries by getting the information;
Name
Description
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Version
Price
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ACC 157: DATA WAREHOUSING AND MANAGEMENT
Module #25
Name: _______________________________________________________________
Section: ____________ Schedule: ________________________________________
Class number: _______
Date: ________________
3) Activity 5: Check for Understanding.
1. Does Excel is good to use for significant data margin, Yes or No and Why?
Answer:
2. What is recommended analytic tool for drag and drop operations?
Answer:
A. LESSON WRAP-UP
FAQs:
1. What programming language is recommended to use in statistical analysis?
Answer:
R is best at statistical analysis, such as normal distribution, cluster classification algorithms, and regression
analysis.
2. This Analytical Tool has three version?
Answer:
POWER BI It comes in three versions: Free, Pro, and Premium. Its PowerPivot and DAX language can
implement sophisticated advanced analytics similar to writing Excel formulas.
In 2016, Data Science Central, an online resource for those who work
with big data, published a blog that listed 15 Astonishing Tweetable Facts
About Analytics. Coming in at 140 characters or less (that makes them
“Tweetable”), these mind blowers might be a little dated two years later,
but they are definitely worth checking out if you are trying to get a grip on
the “enormity and importance” of analytics. They’re also heady small-talk
fodder, perfect for impressing friends and co-workers … and perhaps a
chatty relative or neighbor.
This document is the property of PHINMA EDUCATION
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ACC 157: DATA WAREHOUSING AND MANAGEMENT
Module #25
Name: _______________________________________________________________
Section: ____________ Schedule: ________________________________________
4)
Class number: _______
Date: ________________
Activity 5: Thinking about Learning (5 mins)
Mark the place in the work tracker which is simply a visual to help you track how much work you have
accomplished and how much work there is left to do. This tracker will be part of your activity sheet.
To develop habits on thinking about learning, answer the questions below about your learning
experience.
1. How was the module able to help you learn?
2. What did you realize about the topic?
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