What is Data Analytics?

Data Analytics is a highly sourced field that is focused on helping individuals and organizations make sense of data. Data analysts typically analyze raw data for insights and trends. They use various tools and techniques to help organizations make decisions and succeed.

A Data Analyst is a person who takes the career of Data Analytics.

A data analyst will extract raw data, organize it, and then analyze it, transforming it from incomprehensible numbers into coherent, intelligible information. Having interpreted the data, the data analyst will then pass on their findings in the form of suggestions or recommendations about what the company’s next steps should be.

Importance of Learning Data Analytics:

• Data Analytics is a well paid discipline all over the world. Data Analyst around the world earn greatly
• Data Analytics can be done by anyone irrespective of your degree.
• Data Analytics is widely used across various sectors
• There is continuous demand for Data Analyst around the world
• Data Analyst can work remotely and earn even from home
• It is not a course that requires a long time to learn like a degree.
• Data Analyst are well respected and needed in top companies in the world
• The need for data Analyst will continue to exist as big data keeps been on the rise.
• Benefits of Data Analytics in business is enormous as it greatly helps businesses make informed business decisions

What are the analytical tools used in data analytics?

There are various tools used in data analysis. They utilize tools such as Excel, Python, SQL, Power BI, Tableau.

Microsoft Excel is a software program that enables you to organize, format, and calculate data using formulas within a spreadsheet system. Microsoft Excel may be used by data analysts to run basic queries and to create pivot tables, graphs, and charts.

Python is one of the most in-demand programming languages today and it’s considered the preferred language for machine learning. It stands out for being very flexible, allowing you to build solutions for various use cases. Plus, it’s fairly straightforward to learn and write.  Its a highly used data analytic tool.

SQL for data analysis refers to the database querying language’s ability to interact with multiple databases at once, as well as its use of relational databases. SQL is one of the most commonly used and flexible languages, as it combines a surprisingly accessible learning curve with a complex depth that lets users create advanced tools and dashboards for data analytics.

To create and interact with databases quickly, SQL has been adapted into a variety of proprietary tools, each with its own focus and niche market, including the popular MySQL, Microsoft Access, and PostgreSQL.

While it remains largely popular for its ability to create and interact with databases quickly, SQL is also commonly used because it is a simple language capable of performing surprisingly complex data analysis

Microsoft Power BI:

Microsoft Power BI is used to find insights within an organization’s data. Power BI can help connect disparate data sets, transform and clean the data into a data model and create charts or graphs to provide visuals of the data. All of this can be shared with other Power BI users within the organization.

Tableau:

This is a Visualization tool. Used for creating dashboards and visualizing data

Tableau is a leading data visualization tool used for data analysis and business intelligence.

Average salary of a Data Analyst

The estimated total pay for a Data Analyst is NGN 390,000 per month in Nigeria, moreover abroad, the estimated pay of a Data Analyst is well over $70,000 to $120,000 per year.

Remote jobs abounds that pays over $100 per day.

In Nigeria its fast becoming a great and most wanted IT skill.

• Register with Myteacher for the course programme
• Begin with Analytical tools like Excel, Python
• Then take on SQL (Database)
• Visualization tools: Tableau or Power BI

Why Choose Data Analytics as a Career?

From healthcare and technology, to finance and retail, all organisations are utilising large datasets for various reasons. This presents an exciting opportunity for individuals who want diverse work experience or those who want to specialise in one particular industry. Data analysts have the potential to help shape business decisions by leveraging their knowledge of market trends, customer behaviour patterns, and other key insights.Most entry-level jobs in the field of data analytics command a healthy starting salary, and there is room to grow into significant earning potential over time. Many companies offer competitive salaries that reflect the importance they place on having top professionals conducting their analytics.

What you will learn

A. DATA  ANALYTICS USING EXCEL

1.       Introduction to Excel Application Environment

2.       Formatting Data in Excel

3.       Excel Formulas and their Types

4.       Types of Cell Referencing used in Excel Formulas

5.       Creating Formulas with Excel Mathematical Operators and Functions.

Template designs

6.       Creating and Automating Invoice, Payroll Analysis, School Academic Results, Amortization and Promotion Schedule Templates in Excel.

7.       Creating Functions in Excel.

a.       SUM

b.       AVERAGE

c.       IF, Nested IF

d.       COUNT, COUNTA

e.       VLOOKUP and HLOOKUP

f.       AND and OR

DATA ANALYTICS USING EXCEL

8.       Statistic and Hypothesis Testing Principles and Excel’s Data Analysis Tools pack for Data Analytics.

9.         Formula Auditing, Data Validation and Formula Evaluation Skills.

10.     Creating and Edit Charts (Graphs).

11.     Creating Pivot Tables for Analyzing Data for a CHI Square Test.

12.     Analyzing Data for Descriptive Statistics, Correlation, Covariance and T.

13.     Analyzing Data for Simple Linear Regression Test,  Rank and Percentile.

14.     Analyzing Data for Multiple Linear Regression Test, and ANOVA

15.     DASHBOARD.

B. PYTHON PROGRAMMING FOR DATA ANALYSIS

Course Outline

1. Introduction to programming
2. Python programming language
3. Print statement
4. Literals
5. Variables
6. Data types
7. Conditionals (if statements)
8. Loops (for and while loop)
9. string
10. List
11. Tuple
12. Set
13. Dictionary
14. Error handling
15. Reading and writing files
16. Functions
17. Classes
18. Working with Pandas and Numpy
19. Numpy arrays
20. Panda series
21. Panda Dataframe
22. Pandas read_csv
23. Data exploration with pandas
24. Data cleaning with pandas
25. Data aggregation with pandas
26. Data visualization with matplotlib

SQL FOR DATA ANALYTICS

Creating Database

Connecting database

SQL Queries

Retrieving and Selecting Data with SQL

SQL Joins

Modify tables

Modifying and analyzing Data with SQL

POWER BI (VISUALIZATION)

Data Visualization Using Power BI

Data Cleaning

Storyboard

Visualizations

DAX

Power Query

N/B: This Syllables is subject to subsequent adjustment to fit the current improvements in the course

OFFLINE CLASSES

Offline classes holds at our centers in Port Harcourt:

Myteacher Institute 14 Rumuagholu road Tessy school junction Rumuagholu off Rumuokoro Flyover Port Harcourt

Myteacher Institute opposite Palflox filling station by Police post Eliozu Port Harcourt

We offer:

Certificate Courses (Single Tools)

 Diploma Programme (combination of all the major tools)

On completion of the programme, you will be certified as a Data Analyst

DIPLOMA DATA ANALYTICS

In this course you will take these Tools as one programme for a period of 18weeks (4 months and 2 weeks)

Excel –  1 month

Python- 2 months

SQL – 1 month

Power BI or Tableau – 2 weeks

FULL STACK COURSE FEES: N150,000 , DISCOUNT OF 40%, (N210,000)

Duration: 4 months