10 SEATS

Mr. Mahesh Kumar has 10+ years of experience in Data Science and he is one of the best instructor at digiStacKedu.

- Price : Call Now!
- Module : 32
- Length : 4 Month
- Level : Advance
- Category : Data Science

- Started : 2 JAN 2024
- Shift : 02
- Class : 120

As per the market survey in 2019, 2.9 quintillion bytes of data are present on the internet and Google handles more than 60,000 searches per second, Now the question is how data experts analyze such a huge volume of data that is why we designed the best course of data science where you will learn how professionals analyze and visualize data using machine learning and python programming. Data science is a field where professionals work on unstructured raw data gathered by multiple sources. Data science is the study of mathematics, statistics, and programming.

Machine learning is a popular field in the current industry, Many professionals and students want to make their carer in data science, this course is designed to upgrade your skills in python programming, NumPy, pandas, statistics, tableau, and machine learning algorithms. This is one of the best courses to be a data analyst moreover, you will get a top-level instructor and work on real-time projects with IT experts, take your first step on the road to success.

We will start this course with the basics of python programming and then we will move forward to the python advance level libraries that are required to do data analysis like NumPy, Pandas, and Matplotlib a part from this, you will learn how to analyze different types of data sets and how to visualize and generate reports.

- Introduction to python.
- Variables,data type and operators in python.
- Type casting and user input.
- Basic Problem solving skills in python.
- Working on Python List with its predefined functions.
- Working on Python Tuples with its predefined functions.
- Python loop control structure-for loop and while loop
- Python If-Else Staements with nesting
- Learning python Sets
- Python Dictionary a practical implementation.
- Block Oriented Programming using Python (Functions).
- Python Object Oriented Programming.
- Inheritance in Python.
- File handling using Python.

- Introduction to Numpy.
- Working on NumPy Array.
- Arithmetic Operators on Numpy Array.
- Inspecting NumPy Array.
- Subsetting, Slicing, Indexing on Numpy.
- Manipulation on Numpy Array.
- Stacking and Splitting Numpy Array.
- Aggregate Functions on Numpy Array.
- Copying and Sorting Numpy Array.
- Introduction to Pandas
- Creating DataFrames using Pandas
- Reshaping Data – Changing the layout of a data set
- Handling Missing values using Pandas
- Combining Data Sets using Pandas
- Grouping Data uisng Pandas and Pivot Table.
- Applying functions on Pandas Data Frame
- Plotting Time Series using Pandas

- Introduction to Matplotlib
- Matplotlib basic plotting and containers.
- Working on size of figures and subplots.
- Histograms and bar chart using Matplotlib.
- Time Series chart using Matplotlib.
- Working on Correlation chart or heatmap.
- Stacking and Splitting Numpy Array.
- Setting up Plot Title,Legend and Axes levels.
- Copying and Sorting Numpy Array.
- Linear Regression line using Matplotlib.
- Creating DataFrames using Pandas
- Joining Matplotlib and Seaborn.

- Introduction to Statistics
- Arithmetic Mean,Median and Mode.
- Variance and standard deviation using Excel.
- Discriptive Stats using Excel.
- Probability distribution Concept.
- Continuous Vs Discrete probability distribution.
- Understanding normal distribution Central limit theorem.
- Inferential Statistics-An Introduction to Hypothesis testing.
- Z and t Test using python.
- ANOVA and CHI square Test Examples.
- Pivot Table Concept in Excel.
- Correlation and Co-Variance Concept.
- Introduction to Box Plot and its Python Implementation.

- An Introduction to Machine Learning.
- Supervised vs unsupervised Machine Learning.
- Data Preprocessing and cleaning techniques.
- Regression techniques - Linear,multiple and polynomial regression.
- Machine learning model cost functions.
- An Introduction to classification.
- Underfitting and overfitting in Machine Learning.
- Understanding Confusion Matrix and Roc Curve.
- Building First Classification Model.
- Logistic Regression and its deep Background.
- Building Decision Tree Model.
- Ensemble Learning Techniques.
- Understanding Random Forest.
- Understanding gradient boosted trees.
- Naive Bayes for classification.
- KNN - K Nearest Neighbour.
- Support Vector Machine and its background.
- Advance Regression technique-Ridge and Lasso

- An Introduction to Clustering.
- K-Mean Clustering Machine Learning.
- Hierarchical clustering in machine learning.
- Agglomerative hierarchical clustering in python
- Calculating cost of a cluster.
- Finding the Best Value of K.
- Understanding dendrogram for Cluster Visualization.
- Linkage algorithm in clustering

- An Introduction to Project Development.
- Project Assignments.
- Task Allocation and Project guidance.
- Final Project Submission.

- Introduction to R Programming.
- Variables,data type and operators in R.
- Type casting and user input in R.
- Basic problem solving skills in R.
- Working on R Vectors.
- Understanding Arithmetic Operators.
- Working on Logical Operators.
- How to Create a Matrix in R.
- Matrix Manipulation rbind and cbind functions.
- Understanding Slice a Matrix.
- Working on Factors and Categorical Variables.

- Introduction to Conditional Statements.
- Understanding Basics of If and Else.
- Controlling the flow of program using IF and Else.
- User Input with If-Else
- Nesting of If-Else Staements.
- Practical Demo on If-Else.
- Assignments on If-Else Lecture.
- IF and Else with Vectors.
- Nesting of IF and Else Statements.

- Introduction to Loop in R programming
- Basics of for Loop and While Loop.
- Combining Loop and If-Else Statements.
- Positive and Negative Loop.
- Practical on For Loop in R programming.
- Practical on Nested For Loop in R programming.
- Practical on While Loop in R programming.
- Practical on Nested While Loop in R programming.
- For and while Loop on R Vectors.
- For and while Loop on R Matrix.
- Switch Case Statement in R Programming.

- Introduction to DataFrame in R.
- Creating R Data Frame using Vectors.
- Slicing on R Data Frame.
- Subsetting Operation on R DataFrame.
- nrow() and ncol() operations.
- Getting unique values from R DataFrame.
- Discriptive Stats on R DataFrame.
- dplyr and tidyverse package in R.
- Filter a Data Frame in R.
- Applying user defined functions on R Data Frame..
- Creating a DataFrame from csv File using R Programming.

- An Introduction to Missing Values.
- Working on Data Preprocessing.
- Loading files and generating Missing Values.
- Pipeline Operator in R.
- Checking Null values in R DataFrame.
- Removing Null values from R DataFrame.
- Replacing Null values from Mean and Median.
- Renaming a column of R DataFrame.
- Calculating count of Null values.

- An Introduction to ggplot2.
- Loading Dataset for advance level plotting.
- Working on Historams in R.
- Drawing Correlation Matrix in R.
- Undersatnding bar Plot.
- Working on Mosaic Plot.
- Drawing Scatter Plot using geompoint.
- Linear Line on Scatter Diagram.
- Working on title,subtitle and caption.
- Handling Scale of R Graphs.
- Applying different theme on R Graphs.
- Handling Scale of R Graphs.
- Performing Grouping Opertion on R Graphs.
- Working on Pie Chart in R.
- Working on box plot in R.

- An Introduction to GroupBy Operation.
- Implementing GroupBy in R.
- Selecting and Filtering DataFrame Columns.
- Skip Null values and Calculating Mean.
- Skip Null values and Calculating Median.
- Statistical Operation on GroupBy.
- Sum and count Operation on GroupBy.
- Finding unique values using GroupBy.

- Basics of statistics.
- Understanding variance and standard Deviation.
- An Introduction to Hypothesis Testing
- Null vs alternate Hypothesis.
- Understanding P value.
- Level of significance in statistics.
- Practicals on chi square Test.
- Practicals on one way ANOVA Test.
- Practicals on two way ANOVA Test

- An Introduction to data analysis.
- Understanding machine learning models.
- An Introduction to regression technique.
- Implementing linear and multiple linear Regression in R.
- Implementing polynomial regression in R.
- Introduction to decision trees.
- Implementing decision tree in R Programming.
- Introduction to Support Vector Machine.
- Implementing SVM using R Programming.
- An Introduction K Nearest Neighbour.
- Implementing KNN using R Programming.

- Introduction to Tableau and Data Visulization.
- Tableau architecture.
- Intsallation of Tableau Desktop.
- Understanding Tableau Interface.
- Role of Tableau in Data Science.
- Introduction to different types of graphs.
- Understanding Tableau Show Me Tab.

- Introduction to MySQL.
- Understading Tableau Toolbars,Data Pane and Analytics
- Tableau and RDBMS connections
- Working with Csv file.
- Tableau and Excel Connection.
- Understanding Tableau Data Source.
- Introduction to JSON Files.
- Working with JSON file Using Tableau.

- Introdutcion to Tableau Fields.
- Categorical vs Numerical Columns.
- Conversion of fields in Tableau.
- Working on Histograms and bar chart.
- Working on Time Series chart.
- Working on Box Plot in Tableau.
- Drawing Pie Chart in Tableau.
- Real Time Example of Bubble Chart.
- Drawing Scatter Diagram.

- An Introduction to World Map.
- Loading Files for Tableau Map.
- Understanding the role of lattitude and longitude
- Drawing Map and Showing sales in different contries.
- Working on Tableau Filter
- Applying filters to all Sheets
- Removing filters.
- Handling Missing Values in Tableau.

- Setting levels on Map
- Setting levels on barchat and Pie Chart
- Setting levels on Bubble Chart
- Advance Color Options in Tableau.
- Setting color Scale on Different Graphs.
- An Introduction to classification.
- Working on multiple chart.
- Working on Fonts.
- Showing level based on percentage.

- An Introduction to Data Manipulation.
- Creating bins of numerical fields.
- Sorting Columns and fetching top 10 bins.
- Sorting Columns and fetching bottom 10 bins.
- Creating a cluster.

- An Introduction to Data Anlytics.
- Understading Basics of statistics.
- Working on box plot with medain value.
- Linear and Polynomial Regression in Tableau.

- An Introduction to Tableau Dashboard.
- Dashboard Layouts and Formatting
- Creating Intractive Tableau Dashboard.
- Working on legends, objects, and filters
- Working on slider in Tablesu Dashboard

- Introduction to SQL.
- Structure of RDBMS.
- Introduction to databases.
- What is a Tables and its Structure.
- Understding Table Fields.
- What is Primary Key.

- Installation of MySQL
- Setting a password for SQL.
- Setting port number 3306.
- Understanding SQL Command Line Promt.
- Understanding the Layout of GUI mode.
- Creating first Database.
- Creating First Table.

- Introduction to SQL Query
- Inserting records into SQL Table.
- Updating records of SQL Table.
- Deleting records From SQL Table.
- Fetching records From SQL Table.
- Select Operation with where clasue.
- Select with In and Not-In Operators.
- Selecting different pattern.
- Select Operation with Like and Not Like.
- Select Operation with Regular Expression.
- Select Operation with Limit.

- Introduction to GroupBy Query
- Understanding GroupBy and Count.
- Working on GroupBy on differentn data set.
- Working on OrderBy on differentn data set.
- Working on Nested Queries
- Nested Queries with GroupBy.
- Nested Queries with OrderBy.
- Having Keyword in SQL.

- An Introduction to Join.
- Types of Joins in SQL
- Left Outer Join in SQL
- Right Outer Join in SQL.
- Inner Join in SQL.
- Full Outer Join in SQL.
- Union Opertion in SQL.

- An Introduction to Keys.
- Designing Table with Primary Key.
- Designing Table with Foreign Key.
- Designing Table with Auto increment Columns.
- Describing Table Structure.

- An Introduction to Constraints.
- Applyting Check Constraints on Columns.
- Logical Operatior and Check Constraints.
- Testing Constraints on Table.

- An Introduction to Views.
- Creating a Simple View.
- SQL View of GroupBy Query.
- SQL View of Join Query.
- SQL View of Nested Query.
- Updating Views in SQL.

Our Student reviews after completion of this course.

Best Data Science Course and Best Teachers,got every support by team.best institute i have ever found for online course as they have different teaching style than the others.

Machine Learning part is bit difficult but now i am able to slove ML problems just because of their excellent teachers. finally i will say, 9/10 for digistackedu.

Digistackedu is a good institute for data science course, i got 100% practical sessions and i am 100% statisfied.i will suggest everyone to join this online program as i have never got this type of support anywhere else.

i will give 9/10 for digistackedu and Successfully completed my project on COVID-19 Data Analysis and also got support in project development.I will suggest everyone to join DigiStackEdu.

Truly Speeking, digiStackedu is the one of the best online learning platform as they always support students,instructors Knowlege is really good.I have completed projects and able to solve ML problems.

Yes, you will get industry valid certificate after completeion of this course and you will be tagged as a Data Analyst.

Yes, You can pay your fee in two installments but it depands on the course you selected.

Yes, You can join Data Science online course even if you do not belong from computer science background. Our experts will help you to upgarde your technical skills, you just need some basic computer skills for this course.

This course is 100% practical oriented, you will work on Python, Machine Learning, Statistics, Tableau and Predictive Analytics moreover, you will learn how to use Data Science toolkit for building Machine learning Models.

DigiStackEdu provide cost effective and quality training,We focus on every student and we understand the value of money.Our Trainers are certified and having more than 10+ years of experience in Digital Marketing, Data Science, Java Programming, Web Designing, Big Data and Data Analytics moreover,We have Trained 47,000+ students and professionals who are working in top level IT companies.

DigiStackEdu only provide internship in Data Science, Digital Marketing, Java Programming, PHP programming and Web Development.For more details please contact to our support team.

Data Science and Data Analytics is the most demanding field in the current IT industry and you'll get a lot of job opportunity after completion of this course as Data is tremendously growing at companies server so there is a need of data experts who can manage this huge volume of data.

You can submit your fee after 3 classes, Even after that if you face any issue with in the next 7 days then you can contect to our support team for the return.

We'll start this course by very basic concepts of python programming further we'll move to the advance concept of Data Science like Statistics, NumPY, Pandas, Machine Learning and Tableau.It will take approx 4-5 month to be master of Data Science after that you will get a industry valid cerficate and will get a tag as a Data Analyst.