Best Data Analytics Course Online [Updated in 2023]
FOR BEGINNERS AND EXPERIENCED LEARNERS
Best Data Analytics Course Online [Updated in 2023]
Start your career as a Data Analyst by acquiring the required skills and study different tools like Spreadsheet, SQL, Python, Tableau and Machine Learning concepts along with real-world projects.
5K+ Learners enrolled
Hours of lectures
About the course
This is where you embark on an amazing journey!
Clear your doubts with ease
1:1 sessions over voice call & chat with our teaching assistants
Features that keep you going
A structured curriculum that makes learning easy
Weekly milestones to keep you motivated
Practice code problems of varying difficulty
Compile & run in an integrated coding environment
Industry leading mentors to help you grow
1:1 Mock interviews with resume and career guidance
Structured feedback to make you better
Get a chance to be referred to your mentors’ company
Most flexible program in the industry
Freedom to learn
Watch classes any time at your convenience
Catch up on the course when life is calling you elsewhere
Comprehensive placement package to make you job ready
Get access to an expert placement coach, professional resume & portfolio services, and Hirist spotlight benefits, with close focus on soft skills
Return on investment
students bag dream tech jobs
Download our placement report
This can be your success story!
What are you waiting for? Start your journey towards your dream job today.
"I would like to dedicate my coding journey to Coding Ninjas. I found their courses very helpful in developing my basic programming concepts."
Software Engineer @ Optum
"The course pause feature is a boon to students like me who are pretty irregular with schedules. The course structure helped me learn everything step by step."
Software Development Engineer @ Amazon
Course updated in 2023
Course curriculum for the curious
Introduction to Data Analytics
DATA ANALYSIS AND ITS TYPES
Data and Information - Introduction to the Data , Differentiating between Data and Information, Structure Data, Semi Structured Data , Unstructured Data. Types of Analysis - Introduction to Data Analysis, Descriptive Data Analysis , Diagnostics Data Analysis,Predictive Data Analysis , Prescriptive Data Analysis , Decision Making.
DATA ANALYSIS FRAMEWORK
Framework and Data Processing Methods - Overview of end to end process,V-Lookup (Merging two data) , Handling Missing Value , Data Formatting , Quartile (handling outlier), Feature Engineering. Tools and Technologies - Overview of Spreadsheet , SQL,Python , Tableau , Machine Learning Algorithms.
Data Analytics with Spreadsheet
INTRODUCTION TO THE SPREADSHEET
Operation Of Spreadsheet - Advantages and use of Spreadsheet , Overview of the operation of Spreadsheet, Sorting , Filtering , Conditional Formatting , Handling Duplicates etc. Function of Spreadsheet - Overview of the functions : Sumif, countif, IF, Count , Charts etc.
UNDERSTANDING THE BUSINESS PROBLEM
Introduction to the Business problem - Introduction to the company with the available dataset, Business problem explanation , Description of the Data points. Data Processing : Merging Data - V-Lookup Function , Syntax , Errors of V-lokup , Drawback. Data Processing : Missing Values - Introduction to the Missing values, Missing Value representation , Imputation Method , Average, Standard Deviation , Median
DATA PROCESSING : BUSINESS PROBLEM
Data Processing: Handling Outliers - Introduction to Outliers , Quartile Function, Candlestick Chart , Finding Outliers. Data Processing : Data Formatting - Data Representation, Formatting of data point. Data Processing: Feature Engineering - Introduction to Feature Engineering and its impact on analysis
UNIVARIATE ANALYSIS :BUSINESS PROBLEM
Data Analysis Methods - Introduction to the type of data , Univariate Method ,Bivariate Method ,Multivariate Method. Univariate Methods and Analysis - Continuous Value : Trim mean , Density plot, Frequency table. Categorical value : Pivot Table.
BIVARIATE ANALYSIS :BUSINESS PROBLEM
Bivariate Methods and Analysis - Continuous to Continuous Method, Categorical to Categorical Method, Categorical to Continuous Method, Scatter Plot , Correlation.
Framework of Storytelling - Storytelling for Business Problem.
Step by Step analysis of the Business Problem - Data processing , Univariate Analysis, Bivariate Analysis.
Analysis of World Economic Data .
Data Analytics With SQL
INTRODUCTION TO THE SQL
Introduction to Database - Database Definition , Database type , RDBMS Database. Introduction to SQL - Introduction to SQL , Database creation, Table creation , Create,Update, Delete , Alter.
RETRIEVE AND FILTERING
Retrieving and Filtering the Data - Select and From keyword , Where Clause. Aggregation Operation - SUM,MIN,MAX,AVG ,COUNT.
ADVANCED SQL QUERIES : GROUP BY
Group by operation - Group by clause, having clause ,Order by Clause ,IN, BETWEEN, LIKE .
ADVANCED SQL QUERIES : JOINS
Introduction to Join - Different types of Join : Inner Join, Outer Join , Cross Join, Left Join, Right Join. Its applications.
Supermarket Data Analysis.
Data Analytics With Python
Introduction To Python - First Program in Python, Anaconda and Jupyter Notebook, Variables in Python, Data Types, Python Numbers, Limit of Integers, Arithmetic Operators, Taking Inputs.
CONDITIONAL STATEMENTS AND LOOPS
Conditionals and Loops - Boolean Datatype, Introduction to If-Else, Using Relational and Logical Operators, Using Else If, Nested Conditionals, While Loop, Primality Checking, Nested Loops. Patterns - Introduction to Patterns, First Patterns, Square Patterns, Triangular Patterns, Character Patterns, Inverted Pattern, Reversed Pattern, Isosceles Pattern. More on Loops - For loop & Range Method, Print Multiples of 3, Check if a Number is Prime, Pattern, Break Keyword, Else keyword with loops, Continue keyword, Pass statements
Functions - Functions and how to use them, Why do we need functions, How does function calling works, Functions using strings & lists, Swap Alternate, Scope of Variables, Default parameters in functions. Object-Oriented Programming Systems(OOPs) - Introduction, Create class & object, Instance Attributes, Class Attributes, Methods, Instance Methods, Constructors, Access modifiers, Class Methods & Static Methods.
Strings, List & 2D List - Strings Introduction, Strings inbuilt functions, Strings slicing, Lists Introduction, List inbuilt functions, Taking Input, Difference of Even-Odd, List Slicing, Multi-dimensional Lists. Tuples, Dictionary, and Sets - Tuples, Tuples Functions, Variable-length input and output, Dictionary Intro, Access/looping elements in dictionary, Adding Or Removing Data In Dictionary, Print All Words With Frequency K, Sets Intro, Functions in sets, Sum Of All Unique Numbers In List.
Working With FileS - Introduction, Open and read Text files, Read file line by line, CSV Files, Work with CSV Files, DictReader, Countrywise Killed. NumPy - Introduction, Why NumPy is fast, Create NumPy arrays, Slicing & Indexing, Mathematical Operations - 1D, Boolean Indexing - 1D, Boolean Indexing - 2D, NumPy Broadcasting. Pandas - Introduction to Pandas, Accessing Data in Pandas, Manipulating Data in Data Frame, Handling NAN, Handling Strings in Data. Matplotlib - Plotting Graphs, Customising Graph, Bubble Chart, Pie Chart, Histogram, Bar Graph, How to decide Graph Type. Seaborn - Categorical Distribution Plots, Categorical Scatter plots, Plotting with Categorical Data, Visualizing Statistical Relationships - ScatterPlot, Seaborn vs Matplotlib, Introduction to Seaborn, Starting with Seaborn, Visualizing Statistical Relationships - LinePlot.
Statistics - Introduction of Statistics, Data Types in Statistics, Sample & Population, Simple Random Sampling, Stratified sampling, Cluster sampling, Systematic Sampling, Categories of Statistics. Descriptive Statistics - Measures in Descriptive Statistics, Measures of central tendency, Measures of Spread, Range & IQR, Variance & Standard Deviation, Measure of Position. Introduction to Inferential Statistics - Introduction to Inferential Statistics, Why Inferential Statistics?, Probability Distribution, Normal Distribution, Standard Normal Distribution, Sampling Distribution, Central Limit Theorem. Hypothesis Testing - What is Hypothesis Testing, Null & Alternative Hypothesis, Significance Level, Test statistic, Test Statistic: Critical value & Rejection Region, Test Statistic: Type of Test, Errors in Hypothesis Testing.
Supermarket Data Analysis.
Tableau : Data Visualisation
Introduction to Data Visualisation - Different ways for Data Visualisation, Types Of Data Visualisation, What is Data Visualisation? Importance Of Data Visualisation. Introduction to Tableau - Automatically Generated Fields, Dimension & measure, Tableau Navigation, Data Joins and Union, Connect with Data, Tableau Installation, What is Tableau, Data Types. Tableau Visualisations - Histogram, Bar Chart, Area Chart, Adding customization, Let’s create the First plot, Understanding the Basics of Plotting, Types of charts, Line Chart
Introduction to Machine Learning - Introduction to Machine Learning, Supervised Learning, Steps for Supervised learning, Loading Boston Dataset, Training an Algorithm.
LINEAR AND LOGISTIC REGRESSION
Introduction to Linear Regression - Introduction to Linear Regression, Optimal Coefficients, Cost function, Coefficient of Determination, Analysis of Linear Regression using dummy Data, Linear Regression Intuition. Logistic Regression - Handling Classification Problems, Logistic Regression, Cost Function, Finding Optimal Values, Solving Derivatives, Multiclass Logistic Regression, Finding Complex Boundaries and Regularisation, Using Logistic Regression from Sklearn.
DECISION TREES AND RANDOM FORESTS
Decision Trees - 1 - Decision Trees, Decision Trees for Interview call, Building Decision Trees, Getting to Best Decision Tree, Deciding Feature to Split on, Continuous Valued Features. Decision Trees - 2 - Code using Sklearn decision tree, information gain, Gain Ratio, Gini Index, Decision Trees & Overfitting, Pruning. Project: Decision - Decision Tree Implementation. Random Forests - Introduction to Random Forests, Data Bagging and Feature Selection, Extra Trees, Regression using decision Trees and Random Forest, Random Forest in Sklearn.
PRINCIPAL COMPONENT ANALYSIS
PCA - 1 - Intuition behind PCA, Applying PCA to 2D data, Applying PCA on 3D data, Math behind PCA, Finding Optimal Number of Features, Magic behind PCA. PCA - 2 - PCA on Images, PCA on Olevitti Images, Reproducing Images, Eigenfaces, Classification of LFW Images. Project - Cifar10
Meet the faculty legends that will make you legendary
Co-Founder & Instructor
Python & Machine Learning
Instructor & Founding Member
SQL & Statistics
Exploratory Data Analysis(EDA)
Love from our alumni
It's a great place to learn how to code. The way of teaching and dedication offered towards your development makes it easier to grasp the concepts even for beginners.
The best part of Coding Ninjas is the faculty, I am grateful for all the guidance.
One of the best mentors to guide you are here at Coding Ninjas. Loved the faculty and content, I would ask everyone who wants to learn to program to take up the courses here. Apart from this, practice is the key to ace the skills.
The experience of learning at CN was overwhelming. It shaped my mindset towards solving programming questions in a systematic way that still helps me in all coding scenarios. This approach helped me in the placement season a lot.
A platform having perfectly structured courses to build your programming skills. Mentors are highly skilled and I would recommend any aspiring coder to take up these courses. Grateful for all the learnings here at Coding Ninjas
Coding Ninjas is a great platform to start your journey with coding. I joined them in 2018 and completed the C++ course under Nidhi maam's guidance. The course helped me to understand concepts of DS and algo in-depth and moreover helped me to crack many coding tests and interviews. Whether it's faculty, placement cell, TA support, etc, Coding Ninjas is just the best. In the end, I'd like to thank all my mentors at Coding Ninjas for guiding me throughout.
It was a great learning experience. The kind of content it provides really helps in building your logic and how to approach a problem in real life too.
Ankush sir has done a wonderful job in explaining the core concept of hard topics.
aristos erevna consulting pvt. ltd
The course structure was designed very effectively for both beginners and experienced coders. Support of Mentors and Teaching Assistants helped a lot to improve my coding fundamentals and helping other students enhanced my coding skills.
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