100 Days of Data Analytics: A Complete Guide For Beginners

How to become Data Analyst?

What is the salary of a Data Anlayst?

What are the skills required to become Data Analyst?

How many days will it take to become a Data Analyst?

In order to answer all the above questions and give you a correct pathway, we are here with 100 Days of Data Analytics that will guide you day-by-day on how to become a Data Analyst in 100 days.

Today, almost all companies need people who can understand the data and its flow and work with it. That’s where data analysts come in. Since they can interpret the vast amount of data that companies collect, they are in great demand. If you’re a beginner and thinking about a career in the field of data analysis, you are at the right place as our 100-day data analytics guide would be very beneficial for you. Throughout the following 100 days, we’ll guide you through every step of the necessary knowledge.

In this guide we have first explained the basics of data analytics then eventually we have moved forward in learning various topics that are necessary. You will have a detailed understanding of data analytics by the end and be prepared to begin working in this fascinating sector. Come along with us as we will go further into the topic of data analytics!

What is Data Analytics?

Data Analytics is the process of examining and interpreting data sets to derive meaningful insights, draw conclusions, and support decision-making. In today’s data-driven world, it plays a pivotal role in shaping strategies, optimizing operations, and gaining a competitive edge. The increasing volume of data generated daily necessitates advanced analytical techniques. Data Analytics empowers organizations to make informed decisions, identify patterns, and adapt to changing market dynamics.

Why Data Analytics?

Let’s talk about the importance of data analytics before we go into our 100 Days of Data Analytics Guide. ​Data analytics generally means to obtain or gain information from unprocessed information or data by deep analysis using various tools and technologies and using that information for future aspects. This process basically helps different organizations to gain a competitive edge as the process of data analytics enhances the overall decision-making. 

Our 100-day plan is designed to provide you with a structured learning path covering essential data analytics topics. Each day is dedicated to a specific topic or skill set, gradually building your expertise throughout the program. Here’s a breakdown of what you can expect:

Getting Started with Data Analytics (Days 1-20)

(Day 1-2): Introduction to Data Analytics

• Start by learning the fundamentals of data analytics. Learn about its significance and uses also. Research the trends which are unnoticed, their correlations, and other perspectives that can help in overall decision-making.
• Learn how data analytics can be used in various different fields or domains such as healthcare systems, finance or e-commerce organizations, marketing, and many more. If we take an example learn about how finance organizations use data analytics methods in order to detect fraudulent transactions.
• Take your time to analyze and think about why you need to learn data analytics, whether your interest lies in this field or not, and why you wish to pursue a career in the field of data analytics.

(Day 3-6) : Basics of Statistics

• Understand Descriptive Statistics
  • Mean
  • Median
  • Mode
  • Variance
  • Standard deviation
  • Covariance and Correlation
• Learn Inferential Statistics
  • Hypothesis testing
  • Confidence intervals
  • P-value
• Learn about probability theory and its applications in data analytics.
• Study different types of Probability Distributions
  • Normal
  • Binomial
  • Poisson
• Other known concepts for Data Analytics
  • Central Limit Theorem
  • Population and Sample

(Day 7-15) : Introduction To Python Programming with Data Wrangling

Learn core Python:

• Python Basics
  • Data Types
  • Expressions
  • Variables
  • String Methods
• Python Data Structures
  • List and Tuple
  • Set
  • Dictionary
• Python Programming Fundamentals
  • Conditional Statements
  • Looping Statements
  • Functions
    • User Defined Functions
    • Built-In Functions
  • Objects and Classes
  • List Comprehension
• Oops
  • Python OOP
• File Handling and Exception Handling
  • Python Exception Handling
  • Python File handling

Data Wrangling

Data wrangling basically means cleaning, transforming, and preparing raw data for analysis.

• Learn Pandas, which is one of the libraries of Python that basically provides data analytics tools.
• Study how to read data from multiple different sources like CSV files, Excel spreadsheets, and databases into the Pandas DataFrames.
• Explore various different techniques for cleaning data, including handling missing values, removing duplicates, etc.
• Understand how to manipulate DataFrames using functions in Pandas for filtering, sorting, and aggregating data.

(Day 16-20) : Data Visualization

• Study about different Data Visualization libraries:
  • MatPolib
  • SeaBorn
• Understand how to create different types of plots like scatter plots, bar plots, and histograms in order to visualize data distributions.
• Understand the type of plots and when to use them.

Data Analytics Intermediate : (Day 21-40)

 (Day 21-27) : Introduction to Excel for Data Analysis

• Overview of Excel interface
• Basics of navigating and working with sheets
• Introduction to cells, rows, columns, and ranges
• Understanding basic functions (SUM, AVERAGE, COUNT)
• Working with mathematical and statistical functions
• Introduction to text functions for data manipulation

Advanced Formulas and Functions

• Working with logical functions (IF, AND, OR)
• Exploring lookup functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
• Introduction to array formulas
• Identifying and handling missing data
• Removing duplicates and dealing with errors
• Text-to-columns and data-splitting techniques
• Formatting data for analysis
• Creating basic charts and graphs
• Tips for effective data presentation
• Introduction to PivotTables for dynamic data analysis
• Creating PivotCharts for visual insights
• Customizing and formatting PivotTables and PivotCharts
• Time-saving shortcuts and productivity hacks
• Excel with AI

(Day 28-31) : Exploratory Data Analysis (EDA)

• What is EDA?
• Techniques of EDA
  • Data Visualization
  • Data Summarization
  • Hypothesis Testing
• Brief types of analysis
  • Univariate Analysis
  • Bivariate Analysis
  • Correlation Analysis
  • Outlier Detection
  • Missing Value Imputation
• Learn how to identify patterns, trends, and outlines in the data using EDA.

(Day 32-35) : Statistical Analysis With Python

• Dive deeper into Statistical Analysis using Python libraries:
  • NumPy
  • SciPy
  • StatsModels
• Study common Statistical Testing like:
  • T-test
  • Chi-square test
  • ANOVA

(Day 36-40) : Fundamentals of Machine Learning

• What is Machine Learning?
• Types of Machine Learning
  • Differences between supervised learning, unsupervised learning
  • Reinforcement learning
• ML – Applications
• Getting Started with Classification
• Basic Concept of Classification
• Regression
• Types of Regression Techniques
• Classification vs Regression
• ML | Types of Learning – Supervised Learning
• Underfitting & Overfitting
• Bias Variance trade-off

Also, Learn about the process of training machine learning models using labeled data.

Understand the concepts like:

• Training Set
• Validation Set
• Test Set

Learn Importance of model evaluation using metrics like:

• Classification
  • Accuracy
  • Precision
  • Recall
  • F1-score
  • Confusion Matrix
  • Auc-roc curve
• Regression
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • R-squared
  • Adjusted R-squared

Data Analytics Advanced : (Day 41-70)

(Day 41-45) : Time Series Analysis

• What is Time Series Analysis?
  • Time Series Data
  • Components of Time Series Data
• Decomposition of Time Series
• Forecasting Methods of Time Series
• Implementing Time Series Analysis in Python
• Time Series Models:
  • ARIMA
  • SARIMA

(Day 46-50) : Big Data Analytics

What is Big Data?

Big data is defined as a large and complex collection of data that is very difficult to handle with traditional techniques of data processing. It basically consists of structured, unstructured, and semi-structured datasets. To control, evaluate, and transform it into insights usually more infrastructure is needed.

• What is Big Data Analytics?
• The technology of Big Data Analytics
  • Hadoop
    • Hadoop Ecosystem
    • Hadoop Architecture
    • MapReduce in Hadoop

(Day 51-60) : SQL For Data Analytics

• What is SQL?
  • DDL, DQL, DML, DCL, and TCL Commands
  • WITH clause
  • ALTER (RENAME) 
  • GROUP BY
  • ORDER BY
  • DESCRIBE 
  • INSERT INTO 
  • SEQUENCES
• Advanced SQL
  • Normalization
  • Join (Inner, Left, Right, and Full Joins)
  • Views
  • Trigger  
  • CTE

(Day 61-65) : Data Analytics Tools and Platform

• Explore the various kinds of Tools and Platform that are used in Data Analytics:
  • PowerBI
  • Tableau
  • Google Data Studio
• Learn how to examine and choose the best and most suitable tool or platform based on the specific requirements of a data analytics project.
• Understand the importance of integrating data analytics tools into existing workflows and how to ensure seamless collaboration and data sharing within the team.

(Day 66-70) : Data Mining and Text Analytics

• What is Data Mining?
  • Data Collection
  • Data Preprocessing
  • Pattern Discovery
  • Model Evaluation
• Association Rule Mining

Text analytics involves analyzing unstructured textual data and trends to derive insights.

• What is Text Analytics?
• Text Preprocessing
• Sentiment Analysis
• Name Entity Recognition(NER)
• Topic Modelling

Days 71-100: Real-World Applications with Projects

(Day 71-75) : Case Studies

• Analyze the real-world case studies in various different domains such as finance, healthcare, e-commerce, marketing, ed-tech, etc. in order to understand how data analytics is generally applied in real-world scenarios.
• Learn from successful analytics projects and best practices.

(Day 76-90) : Milestone Project

• Work on a data analytics project that integrates your learning from the past days.
• Choose a dataset of interest and apply various analytics techniques that you have learned in order to derive meaningful insights and results.

Check out “Best Data Analytics Projects with Source Codes [2024]” to discover inspiration for your milestone project.

Day (91-95): Specialization

• Choose a specific area of data analytics such as healthcare analytics, or financial analytics to specialize in.
• Study different advanced topics and tools relevant to your chosen specialization.

Day (96-100): Portfolio Building

• Create a portfolio that basically showcases, your skills, your proficiency your expertise, and your projects created in the field of data analytics.
• Share your portfolio on different platforms which include GitHub and LinkedIn in order to demonstrate your proficiency to potential employers or clients.

Conclusion

In this journey, we have successfully covered a 100-day plan for learning data analytics. We started with the basics of topics like statistics and programming languages like Python then slowly we moved to various different advanced topics like machine learning and big data analytics and covered every aspect in detail. By following this plan, you’ll gain the skills that are basically needed to analyze the data, make informed decisions on it, and finally work on real-world projects. Remember that, learning data analytics is generally a continuous journey that requires continuous practice in a disciplined manner, and staying updated with the latest trends and technologies around is also very crucial.