Self Guide to becoming a Data Analyst
Tags: datascience, python, 100daysofcode, analytics
Published: 2022-08-25 11:46:28

Data analytics is making a new wave in the corporate world as it is the hottest profession in this new era. About 80 percent of the firms across the globe are investing a large part of their earnings into creating a skillful data analytics division, thus hiring the smartest of people in the industry domain.


Wondering how to launch a career in data analytics?. This article will give you a detailed description of;

1. What is Data Analytics?
2. Responsibilities of a data analyst
3. Top essential skills you need to master as a data analyst.



## What is Data Analytics?

Data analytics is a discipline that focuses on collecting, exploring and analyzing large datasets   to uncover hidden patterns, unseen trends, and discover correlations with the objective of extracting valuable insights to make business decisions.

Data analytics leverage a range of data management techniques, including data mining, data exploration,  data cleansing, data transformation and data modeling to ensure robust analysis.

### Responsibilities of Data Analyst
Common responsibilities include;

- Gathering data- Data analysts collect information from various sources using software, conducting surveys, or buying datasets from data specialists.

- Clean Data- Raw data collected might contain duplicates, errors, missing values or outliers. To maintain data quality, data analysts need to ensure that data is clean   through the use of programming and statistical techniques.

- Finding patterns- Data analysts use statistical tools and techniques  to interpret data sets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive analytics efforts in businesses.

- Collaborating with stakeholders- Data analysts Collaborate with programmers, engineers, and organizational leaders to identify opportunities for process improvements, recommend system modifications, and develop policies for data governance 


- Presenting their findings-Communicating results of the findings is the key role of a data analyst. Use of visualization tools like charts, graphs, writing reports and presentations to target partners aid communication.



 

#### Essential Skills for a Data Analyst.

**Math and Statistics**
Data analysis is directly linked to math and statistics. Therefore to leverage this, you need to master the following concepts;
>  
-Fundamentals of statistics for data analytics.
-Descriptive statistics and probability theory.
-probability distributions such normal distribution, binomial distribution and Poisson distribution.
-Hypothesis testing, p-value,  Type I and Type II errors.
-Algebra and Linear Algebra.
-Calculus and discrete mathematics.

[A guide for mastering math and statistics for Data Analysis](https://www.edureka.co/blog/math-and-statistics-for-data-science/)

**Excel(From basic to Intermediate)**

_Spreadsheets are corporate poetry; when constructed elegantly enough, they can be used to communicate sophisticated ideas to audiences who wouldn’t otherwise be receptive to details.”— Eric Seufert_

Without a doubt, Microsoft Excel is the most popular tool for data analysis with built-in pivot tables which is a popular tool for data analysis.

To level up your experience with excel, you need to master the following concepts;

> 
-Introduction to Data Analysis using excel-(fundamentals of spreadsheet applications, Excel interface navigating around with excel worksheet and workbook.)

> 
-Editing text and Formulas-(performing basic spreadsheet tasks such as viewing, entering and editing data, and moving, copying and filling data and how to reference data using formulas)

> 
-Excel functions and Lists.

> 
-Cleaning & Wrangling Data Using Spreadsheets-(Learn importance of data quality, how to import file data into Excel,
fundamentals of data privacy and data validation,  how to remove duplicate and inaccurate data, how to remove empty rows in  data. 
Dealing with inconsistencies in  data ,how to use the Flash Fill and Text to Columns features to help you manipulate and standardize your data.
)

> 
-Analyzing Data Using Spreadsheets-(learn about the fundamentals of analyzing data using a spreadsheet, how to filter and sort data, how to use the VLOOKUP and HLOOKUP reference functions, how to create pivot tables in Excel,  use several pivot table features, Macros and VBA)

> 
-Data Visualization using Excel-(working with charts and templates).

[A comprehensive guide to Microsoft Excel for Data Analysis](https://www.analyticsvidhya.com/blog/2021/11/a-comprehensive-guide-on-microsoft-excel-for-data-analysis/)

**Python Programming**

Python is an interpreted ,high-level ,general-purpose programming language used in web development, data science and creating software prototypes.
To demonstrate that you have a good foundational knowledge of data analysis with Python, you need to master the following concepts;

> 
- Python Syntax and Basics [Introduction to Python](https://developers.google.com/edu/python/introduction#:~:text=the%20next%20section!-,Language%20Introduction,checking%20of%20the%20source%20code.)

> 
- Python Data Structures and Algorithms.[Data Structures and Algorithms in Python](https://dev.to/luxacademy/data-structures-and-algorithms-in-python-2i88)

> 
- Python libraries for Data analysis;
      -Pandas-Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. 
      -**Numpy**-It is a Python library that provides a multidimensional array object and an assortment of routines for fast operations on arrays, including mathematical, logical, sorting, selecting, discrete Fourier transforms, basic linear algebra and many others.
      -**SciPy**-SciPy in Python is an open-source library used for solving mathematical, scientific, engineering, and technical problems.
It allows users to manipulate the data and visualize the data using a wide range of high-level Python commands. SciPy is built on the Python Numpy extension.
      -**Matplotlib** -Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

> 
[Top essential Python libraries for Data analysis](https://www.simplilearn.com/top-python-libraries-for-data-science-article)


**Power BI/Tableau**
Power BI  not only help in generating  quick analytics and insights, but also help on large-scale real-time analytics on large amounts of data.
Master the following concepts;

> 
- Querying and transforming data.
- Data Modelling.
- Calculations and formula.
- Reports and Visualization

> 
[Tableau](https://www.tableau.com/tableau-analysts)

> 
[Getting started with power BI] 
(https://www.analyticsvidhya.com/blog/2021/11/getting-started-with-data-analysis-using-power-bi/)


**Data Preparation and Validation**

Good data preparation and validation allows for efficient analysis thus making data more productive and essential for decision making.
Every data analyst should master the following;

> 
- Data Collection.

- Data discovery and profiling. [Data profiling using Python](https://www.datacamp.com/tutorial/python-data-profiling)

- Data cleansing. [Here is a practical guide to data cleaning in python](https://towardsdatascience.com/practical-guide-to-data-cleaning-in-python-f5334320e8e)

- Data transformation.[How to transform data in Python](https://www.visual-design.net/post/data-transformation-and-feature-engineering-in-python#:~:text=Data%20transformation%20is%20the%20process,engineering%20that%20facilitates%20discovering%20insights.)

- data Validation and Publishing

> 
[An ultimate guide to Data Preparation](https://monkeylearn.com/blog/data-preparation/)


**Exploratory Analysis and Modelling**
Exploratory Data Analysis refers to a set of techniques originally developed by John Tukey to display data in such a way that interesting features will become apparent.

As a data analyst you need to get a detailed understanding of;

> 
- Regression analysis.
- Classification.
- Clustering.
> 
[Exploratory Data Analysis For Data Modeling](https://towardsdatascience.com/exploratory-data-analysis-intro-for-data-modeling-8ff019362371).

> 

**Machine Learning Libraries**
Understanding machine learning libraries enhances your profile as a data analyst. Therefore to level up your skills, the following libraries are very essential;

> 
- Scikit Learn
- PyTorch
- Tensor flow.

Thank you for taking time to read my article 😊😊😊.

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