MY DATA ANALYTICS JORNAL

During my first week of studying data analytics, i got to learn “ What data analytics is”. And it is defined below.

Data analytics is the collection transformation and organisation of facts to draw conclusion, make predictions and drive informed decision-making

KEY WORDS

Collection
Transform
Organisation
Draw conclusion
Make predictions
Drive informed decision-making
What makes you a strong data analyst is not just maths alone its;

Asking the right questions
Finding the best source to answer your question effectively
illustrating your finding clearly in visualization
I also got to learn the different types of business analytics

4 key types of Business Analystics

Descriptive analytics: The interpretation of historical data to identify trends and patterns
Predictive analytics: Centers on taking that information and use it to forecast future outcomes
Diagnostics analytics: Used to identify the root cause of a problem
Prescriptive analytics: Testing an other techniques are employed to determine which outcomes will yield the best result in a given scenario
I learnt that there are 6 phases a data analyst passes through to solve a problem and make DATA DRIVEN INFORMED DECISION MAKING. These phases include:

THE 6 PHASES OF DATA ANALYTICS

Ask phase: Ask questions and define the problem
Prepare phase: Phase data by collecting and storing the information
Process phase: Process data by cleaning and check the information
Analyze phase: Analyze data by finding patterns relationships and trends
Share phase: Share data with your audience
Acts phase: Act on the data and use the analysis results
You can also work with your gut instinct, but your gut instinct needs to be based on the data otherwise you will go off track. Data analysis is rooted in statistics.

I saw something interesting and surprising while studying, which is DATA ECOSYSTEMS, and they also have their elements.

Data ecosystems are the various elements that interact with one another inorder to produce, manage, store, organize, analyze, and share data e.g hardware and software tools and the people that use them.

data can also be found on cloud.

Cloud is a place where data is being kept online rather than a computer hardrive.

While studying as a data analyst people or you might have these COMMON MISCONCEPTIONS.

Data science is thesame with data analytics

Data science creates new questions using data. in otherwords its creating new ways of modeling and understanding the unknown by using raw data while Analyst find answers to existing questions by creating insights from data sources.

Data analysis and Data analytics sound thesame but they are very differnt.

Data analysis is the collection, processing and analyzind of data to make informed decion making while Data analytics is the science of data

One of the powerful ways you can put data to work is with DATA DRIVEN DECISION-MAKING using facts to guide business strategy.

First step in data drivrn decision-making is FIGURING OUT THE BUSINESS NEEDS TO BE SOLVED.

AS AN ANALYST YOU ALSO NEED TO HAVE SKILLS ( ANALYTICAL SKILLS) WHICH IS WHAT ANYONE WHO WANTS TO BE A GOOD OR EXCELLENT ANALYST SHOULD POSSESS.

ANALYTICAL SKILLS are qualities and characteristics associated with problem sloving using facts.

5 essential points to analytical skills

Curiosity
Understanding context (the condition to which something exist or happens)
Having technical mindset (the ability to break things down into smaller steps or pieces and work with them in an orderly and logical way)
Data design ( how you organize information)
Data strategy ( the management of the people, processes and tools used in data analysis)
Analytical thinking involves identifying and defining a problem and solving it by using data in an organized step by step manner. 5 steps of analytical thinking includes

Visualization
Strategy ( helps data analyst see what they want to achieve with the data and how they can get there. it also helps improve the quality and usefulness of the data we collect)
Problem orientation ( it is all about keeping the problem at the top of your mind through out the project)
Correlation ( relationship. it doesnt equal causation)
Big picture and detail oriented thinking ( is like looking at the whole puzzle instead of seeing only little pieces it helps you zoom out and see possibilities and opportunities)
There are also the 5 WHYs which can also help you find the solution or root cause to a problem.

Gap analysis is a method for examining and evaluating how a process works currently in order to get where you want to be in the future.

Data driven decision-making using facts to guide business strategy
Data set a collection of data that can be manipulated or analyzed as can be manipulated or analyzed as one unit
Root cause the reason why a problem occurs
STAGES OF THE DATA LIFE CYCLE

Plan stage: Decides what kind of data is needed, how it will be managed and who will be responsible for it.
Capture: Collect or bring in data from a variety of different sources.
Manage: Care for and maintain the data. This includes determining how and where it is stored and the tools used to do so.
Analyze: Use the data to solve problems make decisions and support business goals.
Archive: Keep relevant data stored for longterm and future reference.
Destroy: Remove data from storage and delete any stored copies of the data.
In order to be able to collect,organize, manage and process data, you need tools . These tools are called DATA ANALYTICAL TOOLS

These tools include;

Spreadsheet: It is a digital work sheet that stores, organizes and sorts data. e.g microsoft excel and google sheets. It allows you to identify patterns and piece the data together in a way that works for each specific data project. They can also creat e excellent data visualization like graphs and charts.
Query: Is a computer programming language that allows you to retrieve and manipulate data from a database. They allow analyst to select, create, add or download data from a database for analysis e.g Microsoft Query Language.
Data Visualization: They turn complex numbers into a story that people can understand. They also help stakeholders come up with conclusions that lead to informed decision effective business strategies e.g maps, graphs, tables.
There is a relationship between Data Analysis process and Data life cycle.

While data analysis process will help you drive your projects and help you help you reach your business gaols, you must understand the life cycle of your data inorder to use that process.

To analyze your data you will need to have a thorough understanding of it. Similarly you can collcet all the data you want, but the data is only useful to you if you have a plan for analyzing it.

It very important as a data analyst to practice FAIRNESS.

FAIRNESS is ensuring that your analysis doesnt create or reinforce bias (create systems that include everyone).

The best practices to ensure fairness you are working are

CONSIDER ALL THE AVAILABLE DATA. We have to decide what data is useful. Often there will be data that isnt relevant to what you’re focusing on or doesnt seem to align with your expetations. But you cant just ignore it. It is critical to consider all the available data so that your analysis reflect the truth and not just your own expectations.
IDENTIFYING SURROUNDING FACTORS. Context is key for you and your stakeholders to understand the final conclusions of any analysis. You must also consider all of the data to get more insights.
INCLUDE SELF REPPORTING DATA. Its a data collection technique where participants provide information about themselves. It is a great wya to introduce fairness into your collection process. People bring conscious and unconscious bias to their observation about the world , including about other people. This method can help you to avoid these observer bias.
USE OVERSAMPLING EFFECTIVELY. It is the process of increasing the sample size of nondominant groups in a population. This can help you better represent them and address imbalance datasets.
Think about fairness from begining to end.
Some questions are more effective than others. A yes or no question is a closed ended question and cannot lead to valuable insights.

Effective questions follow the SMART methodology

-Specific: if the question is too general try to narrow it down by focusing on just one element. But it should not be a closed ended question.

-Measurable: let our questions be measurable. like using figures, quantities etc

-Action-oriented: Let our questions encourage change

-Relevant: When you ask relevant questions it can help you with the problem ou are trying to solve.

-Time bound: it should specify the time to be studied which limits the range of possible data analyst to focus on relevant data.

We need to ask fair questions ( practice fairnes) meaning there shouldnt be any bias in our questions e.g leading your questions towards a certain way and making assumptions.

Some question you might ask when given a project

⭕ OBJECTIVE: what are the goals of the question? What , if, any, questions are expected to be answered?.

⭕ AUDIENCE: Who are the stake holders? Who is interested or cooncerned about the results of the deepdive? Who is the audience for the deep dive?

⭕ TIME: What is the time frame for completion? By what date does this need to be done?

⭕ RESOURCES: What resources are available to accomplish the deep dive?

⭕ SECURITY: Who should have access to the information.

I learnt about HOW DATA EMPOWERS DECISIONS. The two types of data decision making include;

DATA-INSPIRED DECISION MAKING which explores different data source to find out what they have in common
DATA-DRIVEN DECISION MAKING which uses facts to guide business strategies.

Also i learnt about the two types of data which might help us answer alot of different questions. They include;

1) Quantitative data which deals with measurements of numerical facts and ask questions like WHAT?, HOW MANY?,HOW OFTEN?.
2)Qualitative data which deals with explanatory measures and ask questions like WHY?

Also there are 2 types of data presentation tools which are:
-Reports: which are a static collection of data given to stakeholders periodically
-Dashboards: which monitors life incoming data.

3 COMMON TYPES OF DASHBOARDS
i) Strategic dashboards focuses longterm goals and strategy at the
highest level of maintainance.

ii) Operational dashboard which focuses on short term perfomance
tracking and intermediate goals.

iii) Analytical dashboard which consist of data set and the mathematics
used in this set.

I learnt that data can be Big or Small

-SMALL DATA is a set of small specific data points typically involving a short period of time which are useful in making day-to-day decision making.

-BIG DATA is a large complex datasets typically involves long period o times which enables data analyst to address far-reaching business patterns.

As someone who studied ICT in school i definitely know about spreadsheets but have seen mostly MICROSOFT EXCEL.

Recently before begining my data analytics journey i studied more on microsoft excel, and all its formulas and functions and it was a beautiful experience for me having to come accross new formulas and functions i have never seen before and having to explore and do so many things with microsoft excel,

Today i got to learn about how spreadsheets (microsoft excel and google sheets) can be used to analyse data and how it is related to the data life cycle which include:
➡️ Plan
➡️ Capture
➡️ Manage
➡️ Analyze
➡️ Archive
➡️ Destroy

— — PLAN means formatting your cells, the headings you choose to highlight, the color scheme,and the way you order your data points.

— -CAPTURE data by the source by connecting spreadsheets to other data sources such as an online survey application or database

— -MANAGE different types of data with a spreadsheet. This can involve storing, organizing, filtering and updating information. They can also let you kno who can access the data.

— -ANALYZE data in a spreadsheet to help make better decisions. Some of the most common spreadsheet tools include Formulars and pivot tables.

— -ARCHIVE any spreadsheet that you dont use often but might need to reference later with built-in tools

— -DESTROY your spreadsheet when you know you are certain that you will never need again. If you do have use for it, better have back up copies or for legal security.

⭕ OPERATOR is a symbol that name the type of operation or calculations to be made.
⭕ CELL REFERENCE is a cell or range of cells in a worksheet that can be used in a formular
⭕ RANGE is a collection of 2 or more cells.

I learnt that even as an experienced data analyst there are some common errors that can be made when doing operations. These errors include

1) DIV ERROR
2)DN ERROR
3) N/A
4)REF ERROR

I used excel formulars to calculate data. I used basic formulars like SUM, AVERAGE, MIN, MAX, DIVIDE,MULTIPLY etc amongst the many formulars that exist.

I also learnt about thinking which is the process of recognizing the current problem or situation, organizing available information, revealing gaps and opportunities and identifying the options.

One way you can practice structured thinking and avoiding mistakes is
by using a SCOPE O WORK.

⭕ SCOPE OF WORK (SOW) is an agreed upon outline of the work you are going to perform on a project e.g work details, reports, schedules that the client can expext. Under this we have:

➡️ DELIVERABLES which focuses on What work is being done and what things are being created as a result of this project?
➡️ MILESTONES which is closely related to your timeline. what are the major milestones in your project? How do you know when a given part of your project is complete
➡️ TIMELINE which is closely tied to the milestone you created for your project.The timeline is a way of of mapping expectations for how long each step of the process should take
➡️ REPORTS you have to give status update to your stakeholders. will it be weekly or monthly ?.

The different stakeholders we might encounter while working on a project. They inlcude:

➡️ The executive team. Who provide strategic and operational
leadership to the company. They are made of VICE PRESIDENTS, CHIEF
MARKETING OFFICER, AND SENIOR LEVELS PROFESSIONALS.
➡️ The customer-facing team. Anyone in an organization who has some
levels of interaction with customers and potential customers.
➡️Data science team. Organizing data within a company takes teamwork.
There is a good chance you will find yourself working with other data
analyst, data scientist and data engineers.

I have learnt that working effectively with stakeholders you’ll often have to go beyond the data and to do that we will need the following tips to communicate clearly, establish trust an deliver your findings accros groups
‼️ Discuss goals
‼️Feel empowered to say no
‼️ Plan for the unexpected
‼️Know your project
‼️Start with words and visuals
‼️ Communicate often.

Clear communcation is key. Before you communicate think about
— WHO YOUR TARGET AUDIENCE IS
-WHAT THEY ALREADY KNOW
-WHAT THEY NEED TO KNOW
— HOW OFTEN CAN YOU COMMUNICATE THAT EFFECTIVELY TO THEM

In todays studies i learnt that good writing, listening and speaking skills can also help you effective communication as a data analyst. This is because speed can be the enemy of accuracy .

This is why communication is the most valuable tools for work with teams. So it is important to start with structured thiking and a well planned SCOPE OF WORK(SOW).

I also learnt that data can have limitations. As a data analyst it is important to know the limits of dat aso you can prepare for it. Limitations of data are as follows,

⭕ Incomplete or nonexistent data.
⭕Dont miss misaligned data
⭕ Deal with dirty ( data cleaning)
⭕ Tell a clear data;

💠 compare thesame types of data
💠 visualize wit data
💠 leave out needles graphs
💠 test for statistical significance
💠 pay attention to sample size

⭕ Be the judge