Tools and languages covered
Python- NLP
- Data Science
- Deep Learning
- ANN
- Matplotlib
- Tableau
- SQLite Database
Python
NLP
Data Science
Deep Learning
ANN
Matplotlib
Tableau
SQLite DatabaseOverview of Tableau for Data Science Course
We will cover Tableau Desktop, Tableau Server, Visual Analytics Best Practices, and Tableau Prep in this Tableau training, as well as various hands-on projects as an assignment with real-world practice covered in the classroom lectures. There are no programming skills required.
- Tableau is a visual analytics tool that is revolutionizing how we utilize data to address issues by enabling individuals and companies to maximize their data.
- Tableau with data science specializes in data analytics and assists the data science team in digging deep into the data to find patterns and insights. Tableau may also be used in data science to allow data analysts and data science experts to apply various algorithms to usefully extracted data.
- Tableau calculates the fields first, and then Python is used to specify them. As a result, you may seamlessly use the power of numerous libraries and functions from Tableau’s visualizations. Using new models, you can apply capabilities like Machine Learning, Predictive Analytics, Sentiment Analysis, and Time Series Forecasting in Tableau’s Calculated Fields.You may accomplish this using TabPy, an API that allows you to evaluate Python code from within a Tableau.
- You will become an expert in Tableau for Data Science Training with the help of the BTree systems Training technique. This Tableau Data Science certification will assist students in becoming industry-ready for top data-related job roles. Data science and business intelligence workers frequently use Tableau, one of the most well-liked data visualization tools available today. It lets you build interactive and colorful maps that are smart and impactful. The creation of conventional graphs and charts is not its only application.
Corporate Training Program
Enhance your employee’s skills with our learning programs and make your team productive.
The Learners Journey
We will prepare you on how to face Tableau for Data Science interviews along with this you will also have the process like students enquire, counseling, live demo, admission process, evaluation, certification, interview, and placement support.
Curriculam of Tableau for Data Science Training
Data Science Preliminaries
- Data Science Introduction
- Data Science Process
- Data Statistics – Descriptive and Inferential
- Data Visualization
- Machine Learning Algorithm in Detail
- Supervised Learning Algorithm
- Unsupervised Learning Algorithm
- Reinforcement Learning Algorithm
- Data Science Importance and Key challenges
- Life of Data Scientist
- Data Science Real-Time Application
- Python Introduction and its IDE
- Why Python and its Python Platform
- Different Flavours of Python
- Understanding of Anaconda Navigator
- Understanding of Python IDE-Spyder
- Setting up working Directory
- Python Packages
- Numpy and Pandas
- Matplotlib, Scipy and Sklearn
- Python Programming
- String – Immutable, Count, Indexing, Transversal, Sequencing and Slicing
- Tuples – Mutable, Sorting, Sequencing
- Lists – Append Method
- Dictionaries – Reassigning and Removing
- Aliases and Clones
- Sets – Immutable and Boolean Operations
- Data Structure using Numpy Package
- Introduction to Numpy Package
- Mathematical and Statistical function using Numpy
- Array – Creation, Concatenation and Selection
- File – Open, Read, Write and Close
- Data Processing in Files using Python
- Exception Handling in Files using Python
- Data Frame using Pandas Package
- Introduction to Pandas Package
- Data frame – Read, Select and Filter
- Handling Missing and Duplicates Data
- Data frame Joins – Inner, Outer, Left and Right
- Combing and merging Data Set
- Data Preparation
- Data Preparation Process
- Coding, Transcribing and Data Cleaning
- Statistically Adjusting the Data
- Selecting a Data Analysis Strategy
- Classification of Statistical Technique – Univariant and Multi Variant
Data Statistics & Visualization
- Descriptive Statistics Modules using Python
- The Measure of Central Tendency
- Mean and Weighted Mean and Geometric Mean
- Median, Mode, Percentiles and Quartiles
- Measure of Dispersion
- Variance, Standard Deviation and Range
- Interquartile Range and Coefficient of Variation
- Numerical Measures: Z-Scores, Chebyshev’s Theorem, Empirical Rule
- The Measure of Detecting Outliers
- Exploratory Data Analysis – Five – Number Summary, Box Plot
- Measures of Association: Covariance and Correlation Coefficient
- Hypothesis Testing
- Introduction of Hypothesis Testing
- Formulation of Hypothesis
- Selection of Statistical Test
- Critical Value Approach in Hypothesis
- P-Value Approach in hypothesis
- Type I and Type II Error
- Real-Time Application of Hypothesis Testing
- Inferential Statistics using Python
- Non – Parametric Statistical Test
- Wilcoxon Sign Test and Friedman Test
- Mann – Whitney Test and Kruskal – Wallis Test
- Chi-Square Test
- Parametric Statistical Test
- T-test (One and Two Sample
- Z – test (One and Two Sample)
- F – Test (One and Two Sample)
- Data Visualization – Matplotlib Package
- Data Visualization using Matplotlib Packages
- Introduction to Matplotlib
- Line Plots and Bar Charts
- Pie Chart and Histogram
- Scatter Plots and Scatter Plot
- Advanced Plotting
- Exporting Plots and Other Plotting Packages
Supervised Learning Algorithm
- Linear Regression using Python
- Linear Regression Analysis
- Formulation of Regression Model
- Bivariate Regression with Real-Time Example
- Statistics Associated with Bivariate Regression Analysis
- Conducting Bivariate Regression Analysis
- Multiple Regression with Real-Time Example
- How Linear Regression is used for Prediction
- Multicollinearity, Heteroskedasticity and Auto-Correlation
- Real-Time Application of Bivariate and Multiple Regression in Real Estate Analytics
- Logistic Regression using Python
- Logistic Regression Introduction
- Formulation of Single and Multiple Logistic Predictor Model
- How Logistic Regression is used for Classification
- Estimated Equation for Logistic Regression
- Real-Time Application of Logistic Regression in Banking Analytics
- Linear Discriminant Analysis
- Linear Discriminant Analysis Model
- Two Group Discriminant Analysis
- Multiple Group Discriminant Analysis
- Statistics associated in Linear Discriminant Analysis
- Real-Time Application of Discriminant Analysis in Hospitality Industry
- Linear Discriminant Analysis
- Naive Bayes Introduction
- Probabilistic Classification in Naïve Bayes
- How Naïve Bayes can be used for Classification
- Real-Time Application in Financial Fraudulent Classification
- Real-Time Application in Ball Badminton Game Classification
- Advantage and Shortcoming of Naive Bayes
- K-Nearest Neighbour (KNN)
- K – Nearest Neighbour Introduction
- How KNN can be used for Classification
- How to measure “Nearby” record using Euclidian Distance
- Choosing “K” and High “K” vs. Low “K”
- Real-Time Application using KNN
- Implementation of Confusion Matrix using Python
- Support Vector Machine (SVM)
- SVM Introduction
- Linear SVM – Hyper Plane Classification
- Non-Linear SVM – Kernel Trick Classification
- Real-Time Application of SVM
- SVM Advantages and Disadvantages
- Decision Tree (Classification Tree)
- What is a Decision Tree
- How Decision Tree is used for Classification and Prediction
- Choosing and identifying attributes for Decision Tree
- Gini Index, Entropy and Information Gain with Intuitions
- Decision Tree Pruning Methods
- Forward Pruning – Pre pruning
- Backward Pruning – Post Pruning
- Subtree Replacement
- Sub Tree Raising
- Real-Time Application of Decision Tree in Survival Analysis
- Random Forest Algorithm
- Random Forest Introduction
- Ensemble Method – Random Forest
- Choosing Best Predictor Variable for Decision Tree
- Real-Time Application of Random Forest
- Analysis of Variance
- Conducting One-Way Analysis of Variance
- Statistics associated with ANOVA
- Conducting Two Way and Multi-Way Analysis of Variance
- Real-Time Application of Analysis of Variance
- Analysis of Covariance
- Conducting Analysis of Co-Variance
- Statistics associated with ANCOVA
- Conducting ANCOVA using Python
- Real-Time Application of Analysis of Co-Variance
- Time Series Methodology
- Time-series Basics
- Time-series Component
- Trend Component and Seasonal Component
- Cyclical Component and Irregular Component
- Smoothing Methods
- Moving Average Method
- Exponential Smoothing method4Trend Based Forecasting
- Linear trend Forecasting
- Non-Linear Trend Forecasting
- Exponential Forecasting
- Autoregressive Moving Average (ARIMA) Model
Unsupervised Learning Algorithm
- Principal Component Analysis (PCA)
- Factor Analysis Introduction
- Statistics associated with Factor Analysis
- Factor Analysis Methods
- Extraction Method – Principal Component Analysis
- Rotation Method – Varimax Rotation
- Factor Loading and Factor Matrix
- Real-Time Application of Factor Analysis
- Cluster Analysis
- Cluster Analysis Introduction
- Statistics associated with Cluster Analysis
- How Cluster Analysis is used for Market Segmentation
- Classification of Clustering Methods
- Hierarchical Clustering
- Non-Hierarchical Clustering (K-Mean Clustering)
- Representation of Clustering
- Agglomeration schedule and Dendrogram
- Real-Time Application of Cluster Analysis
- Association Rule – Python
- Association Rule Introduction
- Apriori Algorithm
- How to build the Recommendation System
- Multiple Association Rules
- Real-Time Application of Apriori Algorithm in Amazon
- Real-Time Application of MBA in Retail Sector
- Correlation Algorithm
- Correlation Analysis
- Formulation of Correlation Matrix
- Product Moment Correlation
- Partial Correlation
- Non-metric Correlation
- Real-Time Application of Correlation
Python Looping Concepts
- Control Structures in Python
- Execution of if Loop, IF-ELSE Loop
- Execution of Shorthand IF Loop
- “AND” (and) “OR” Condition in if Loop
- NESTED IF Loop and PASS Function
- WHILE Loop with BREAK and CONTINUE Function
- WHILE LOOP with ELSE
- FOR LOOP with BREAK and RANGE function
- User-Defined Function using Python
- Create a function and call a function
- Passing an argument to the function
- Return function and Passing Arbitrary Arguments
- Use of this Keyword Arguments
- Arbitrary Keyword Arguments
- Default Parameter
Deep Learning Concepts
- Deep Learning Introduction
- Visual Introduction about Deep Learning
- Deep Learning Architecture
- Artificial Neural Network (ANN)
- Convolution Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Deep Mind Deep Q-Learning
- Application of Deep Learning
- Artificial Neural Network (ANN)
- ANN – Architecture and Schematic Diagram
- ANN – Architectural Types:
- Single Layer Feed Forward
- Multiple Layer Feed Forward
- Pre-processing steps of ANN
- Backpropagation Algorithm
- Real-Time case study using ANN
- Advantage and Disadvantage of ANN
- What ANN can do and What Not
- Comparison of ANN and Digital Computers
- Application of Artificial Neural Network
- Image Processing and Image Extraction in Python
- What is an image? – Python
- How do we represent images in computers? – Python
- How can we analyse the image?
- Feature Extractors
- Hue Histogram
- Edge Histogram
- HAARlike
- Using Classifiers for image classification
- Image Processing and Object Recognition in Python
- What is Object Recognition and why do we need it
- Detection of object – Viola-Jones Algorithm
- Build / Train Object Model
- Window Based Object Detection
- Haar – Features and Integral Image
- Feature Selection and Adaboost
- Natural Language Processing (NLP)
- Introduction about NLP
- NLP-Embedding
- NLP-Word2Vec
- NLP-Thouhtvectors
- Text Analytics
- Application of NLP
Advanced Data Science Concepts
- Data Science – Measurement and Scaling
- Measurement and Scaling Introduction
- Primary Scales of Measurement
- Nominal Scale and Ordinal Scale
- Interval Scale and Ratio Scale
- Comparative Scaling Techniques
- Paired Comparison Scaling
- Rank Order Scaling
- Constant Sum Scaling
- Q-Sort and Other Procedures
- Non-Comparative Scaling Techniques
- Continuous Rating Scale
- Itemized Rating Scale
- Likert Scale
- Semantic Differential Scale
- Stapel Scale
- Inferential Statistics – Probability and Bayes’ Theorem
- Probability and Statistical Experiment
- Counting Rule – Permutation and Combination
- Assigning Probabilities – Classical, Frequency and Subjective method
- Events and Their Probabilities
- Relationships of Probability – Union, Intersection, Compliments and Mutually Exclusive events
- Conditional Probability and Bayes’ Theorem
- Discrete Probability Distribution
- Discrete Probability Distribution
- Random Variable – Discrete and Continuous
- Binomial Probability Distribution
- Evans Electronics Real-time example using Binomial Probability
- Poisson Probability Distribution
- Mercy Hospital Real-time example using Binomial Probability distribution
- Hypergeometric Probability Distribution
- Neveready’s Hospital Real-time example using Binomial Probability
- Continuous Probability Distribution
- What is Continuous Probability distribution?
- What is Uniform Probability Distribution?
- Slater’s Buffet Real-time example using Uniform Probability Distribution
- Normal Probability Distribution
- Pep Zone Real-time example using Normal Probability distribution
- Exponential Probability Distribution
- Real-time example using Exponential Probability distribution
- Data Preparation
- Data Preparation Process
- Coding and Transcribing
- Data Cleaning and Statistically Adjusting the Data
- Selecting a Data Analysis Strategy
- Primary and Secondary Data
- Primary Data Collection
- Secondary Data Collection
- Comparison of Primary and Secondary Data
- Classification of Secondary Data
- SQLite Database Integration with Python
- SQLite Database Installation steps
- Python interface for SQLite
- SQLite CRUD Operations
- Loading Data set through SQLite3 Package
- Database Management through Python
- Questionnaire Design
- Questionnaire Design Process
- Specify the Information Needed
- Type of Interviewing Method
- Individual Question Content
- Overcoming Inability Unwillingness to Answer
- Choosing Question Structure
- Choosing Question-Wording
- Determining the Order of Question
- Form and Layout
- Reproduction of the Questionnaire
- Pretesting
Data Visualization using Tableau
- Tableau Basics: Your First Bar chart
- The Business Challenge – Who Gets the Annual Bonus
- Connecting Tableau to a Data File – CSV File
- Navigating Tableau
- Creating Calculated Fields
- Adding Colours
- Adding Labels and Formatting
- Exporting Your Worksheet
- Time series, Aggregation, and Filters
- Working with Data Extracts in Tableau
- Working with Time Series
- Understanding Aggregation, Granularity, and Level of Detail
- Creating an Area Chart & Learning about Highlighting
- Adding a Filter and Quick Filter
- Tableau – Maps, Scatterplots, and Your First Dashboard
- Joining Data in Tableau
- Creating a Map, Working with Hierarchies
- Creating a Scatter Plot, Applying Filters to Multiple Worksheets
- Let’s Create our First Dashboard!
- Adding an Interactive Action – Filter
- Adding an Interactive Action – Highlighting
- Joining and Blending Data, PLUS: Dual Axis Charts
- Understanding how LEFT, RIGHT, INNER, and OUTER Joins Work
- Joins with Duplicate Values
- Joining on Multiple Fields
- The Showdown: Joining Data vs. Blending Data in Tableau
- Data Blending in Tableau and Dual Axis Chart
- Creating Calculated Fields in a Blend (Advanced Topic)
- Section Recap
- Table Calculations, Advanced Dashboards, Storytelling
- Downloading the Dataset and Connecting to Tableau
- Mapping: how to Set Geographical Roles
- Creating Table Calculations for Gender
- Creating Bins and Distributions for Age
- Leveraging the Power of Parameters
- How to Create a TreeMap Chart
- Creating a Customer Segmentation Dashboard
- Advanced Dashboard Interactivity
- Analysing the Customer Segmentation Dashboard
- Creating a Storyline
- Advanced-Data Preparation in Tableau
- What Format Your Data Should Be In
- Data Interpreter and Pivot
- Splitting a Column into Multiple Columns
- Metadata Grid and Fixing Geographical Data Errors in Tableau
Tableau Dashboard Concepts
- Map creation in Tableau dashboard
- Maps, Scatterplots, and Your First Dashboard
- Joining Data in Tableau
- Creating a Map, Working with Hierarchies
- Creating a Scatter Plot
- Tableau Cluster Creation and Modelling
- Cluster Analysis Introduction
- Statistics associated with Cluster Analysis
- Conducting Cluster Analysis
- Classification of Clustering Procedure
- Hierarchical Clustering
- Non-Hierarchical Clustering
- Tableau Regression Analysis
- Linear Regression Analysis
- Formulation of Regression Model
- Bivariate Regression
- Statistics Associated with Bivariate Regression Analysis
- Conducting Bivariate Regression Analysis
- Multiple Regressions and Conducting Multiple Regression
- Mapping Bivariate Regression with Real-Time Example.
Tableau Step Up & Groups
- Basics of Tableau Step up
- Tool Tip Analysis and Grouping
- Table Calculations, Advanced Dashboards, Storytelling
- Downloading the Dataset and Connecting to Tableau
- Mapping: how to Set Geographical Roles
- Creating Table Calculations for Gender
- Creating Bins and Distributions for Age
- Tableau – Groups and Sets
- Project Brief: 1,000 Start-ups
- Working with Groups
- Creating Static Set and Dynamic Set
- Combining Sets and Controlling Sets with Parameters
- Dashboard: The Start-up Quadrant Preview
- Dashboard Tricks
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Mentor Profile of Tableau for Data Science Certification
- The Tableau Trainers offer a thorough introduction to the Tableau tool with the ideal ratio of practical and theoretical sessions.
- Our Trainers are real-time Tableau professionals from the BI area who help to learn participants improve their expertise through hands-on learning approaches.
- Our Trainers have more than 12 years of hands-on experience and are experts in the fields of data science and tableau.
- Our Trainers have extensive academic and practical knowledge and are employed by prestigious organizations like Accenture, Infosys, and Facebook. They offer expert support to students on an individual basis to prepare them for course-related job interviews.
- Our Trainers at BTree help students strengthen their resumes and boost their confidence by immersing them in interview training activities and group discussions.
Tableau for Data Science Industrial Projects
Crime analysis dashboard
This project, which is based on the issue description, will examine a dataset of various crimes that have occurred in a certain location.
Air quality and pollution analysis dashboard
From the environmental protection sector comes this project to examine a dataset about many aspects of local pollution and air quality
Stack market dashboard
In the project, the data volume and data fluctuate significantly with the stock market generating a lot of data that can be analyzed.
Twitter sentiment dashboard
Sentiment analysis is one of the most frequent data analytics issues that have been resolved for the social media sector.
Key Features of Tableau for Data Science Training
Real-Time Experts as Trainers
You will get the convenience to Learn from the Experts from the current industry, to share their Knowledge with Learners. Grab your slot with us.
Live Project
We provide the Real-time Projects execution platform with the best-learning Experience for the students with Project and chance to get hire.
Placement Support
We have protected tie-up with more than 1200+ leading Small & Medium Companies to Support the students. once they complete the course.
Certifications
Globally recoganized certification on course completion, and get best exposure in handling live tools & management in your projects.
Affordable Fees
We serve the best for the students to implement their passion for learning with an affordable fee. You also have instalment to pay your fees.
Flexibility
We intend to provide a great learning atmosphere for the students with flexible modes like Classroom or Online Training with fastrack mode
Bonus Takeaways at BTree
- Extensive training in Tableau for Data Science and exam experience.
- Interactive and real-time data science tools.
- 100% placement assistance and resume building.
- Access secured recording sessions both online and offline.
- EMI Options for both Debit and Credit.
- Career guidance camp for passed-out students and IT or Non-IT Students.
- Free lifetime study material
- Guidance for interviews, portfolios, live projects, etc.
Certification of Tableau Data Science Course
- Tableau for Data Science Course Certification is one of the professional recognitions that demonstrate the participant has gained a substantial understanding of Tableau Software and its Applications.
- This certificate certifies that the participant has gained the necessary abilities to function as a Tableau Professional by providing real-time Data Visualization & Business Intelligence project experience at the end of the Tableau Course at BTree.
- In addition to helping the interviewer prioritize your profile, including this Tableau for Data Science Course completion certificate with your resume also makes it possible for you to pursue a wide range of professional options in the business intelligence industry.
Placement Process
Course Registration
Our Team will help you with the registration process completely along with free demo sessions.
Training Stage
Every course training is built in a way that learners become job ready for the skill learned.
Job Opportunities
Along with our expert trainers our placement team brings in many job opportunities with preparation.
Placement Support
Get placed within 50 days of course completion with an exciting salary package at top MNCs globally.
Career Path after Tableau for Data Science Training in Chennai
Annual Salary
Hiring Companies
Annual Salary
Hiring Companies
Annual Salary
Hiring Companies
Tableau for Data Science Training Options
Our ultimate aim is to bring the best in establishing the career growth of the students in each batch individually. To enhance and achieve this, we have highly experienced and certified trainers to extract the best knowledge on Tableau for Data Science Certification. Eventually, we offer three modes of training options for the students to impart their best innovations using the Data Science tools & course skills. For more references and to choose a training mode, Contact our admission cell at +91-7397396665
Online Training
- 45+ hours of e-Learning
- Work on live Tableau for Data Science tools
- 3 mock tests (50 Questions Each)
- Work on real-time industrial projects for Tableau for Data Science
- Equipped online classes with flexible timings
- 24×7 Trainers support & guidance
Self-Paced Training
- 45+ hours of Tableau for Data Science classes
- Access live tools and projects
- 3 Mock exams with 50 Questions
- Live project experience
- Lifetime access to use labs
- 24×7 Trainers & placement support
Corporate Training
- 40 hours of immense corporate training
- Support through our expert team
- 3 Mock exams (60 questions each)
- Work on real-time Tableau for Data Science projects
- Life-time support from our corporate trainers
- 24×7 learner aid and provision
Get Free Career Consultation from experts
Are you confused about choosing the right and suitable course for your career? Get the expert’s consultation to pick the perfect course for you.
Additional Information
Advantages of using Tableau with Data Science
- Less code and better visualization
• The exploration and visualization of data are the core goals of Tableau. In larger data science programming languages like R and Python, where the caliber of third-party graph-generation tools is not necessarily the best, these are significant but secondary goals. - Superior exploration of data
• It is challenging to replicate in the native SDKs and APIs of popular data science languages the ability to express initial data loads before modeling, which is particularly valuable for evaluating the method to be pursued. - Indigenous clustering
• Whether the data is being imported from flat files or is being brought in live by a service, cluster analysis is a native feature in Tableau and a one-click application in a dashboard.
Future scope of Tableau with Data Science
- Who wouldn’t test a tool if it offered interactive dashboards, quicker reports, and reliable data? As a result, every organization is using this platform to make informed decisions and quickly acquire actionable information. The advantages of Tableau are already being reaped by large corporations. The use of this instrument is being gradually adopted by all businesses. So, as time goes on, there is a growing need for Tableau specialists. We may now see a wide range of coaching organizations generating several Tableau courses for novices.
Salary packages for Tableau for Data Science Developers
- Tableau Developer and Data Analysts Payscale for freshers:
- The average yearly income for a data analyst and tableau developer in India is 5.0 lakhs, with salaries ranging from 3.9 lakhs to 18.2 lakhs. Salary projections are based on 17 salaries provided by Tableau developers and Data Analysts.
- Tableau Developer and Data Analysts Payscale for Experienced:
- With less than two years of experience to ten years of experience, Data Analyst and Tableau Developer salaries in India range from 3.9 lakhs to 18.2 lakhs, with an average yearly pay of 5 lakhs based on 17 salaries.
Tableau vs Python
- Usage
- • Python is a high-level programming language that is used to create software applications that fix computer issues. It is renowned for the readability of its code when given ample whitespace.
- • It includes designs that facilitate straightforward programming on both small and big scales. Tableau is a platform for data visualization that aids in information interpretation and the creation of effective and valuable business insights.
- • It is employed to assess and examine the connection between databases and the data containing items, places, and years.
- • From its unique in-memory data engine, Tableau gathers a considerable amount of data, which it then saves and recovers. Tableau is renowned for producing exceptional user interfaces as well.
- Data Management
- • Python is the ideal language to use if you need to handle streaming data. Even if the type of data you have is obscure, you can simply locate a module to parse it because of the vast user data that Python has available.
- • The required libraries or packages to support the data types can be used to indirectly load various data kinds. Tableau is renowned for its possible connections that are not immediately obvious. It is possible to immediately ingest several file formats.
- • Additionally, it can link to other kinds of databases. Its built-in connections enable you to access a variety of services. It has excellent features and is very adaptable. It can import several data formats, including text files, JSON, xlsx, CSV, etc., and produce visualizations.
- Visualization
- • Python is a general-purpose programming language that may also be applied to data analytics services. Python is capable of creating visuals, but the procedure is highly difficult and time-consuming.
- • Python can produce data visualizations by using open libraries like MatPlotLib, SeaBorn, ggPlot, etc. A tableau is an interactive tool for data visualization that is frequently used in business intelligence.
- • Data visualization starts right away thanks to user-friendly features like drag and drop that make it simple to create high-value graphics. Tableau is essentially a user-friendly data visualization tool. Nearly all of the visualizations required for your standard upfront business reporting are already available.
- Integrations
- • Python is a versatile, approachable language with a sizable standard library. Python was created using an open-source license from the Open Source Initiative. As a result, it may be freely used and distributed for business objectives as well.
- • With Tableau, the framework can be integrated with the most popular databases to ingest data and work toward making it incredibly scalable. Among the databases are MySQL, Amazon Redshift, Google BigQuery, etc.
Tableau vs Tableau CRM
- • A tableau is software that works for all sectors and businesses of all sizes. Anyone may use it almost anywhere to see and analyze their data! Tableau takes data and information from multiple sources and compares them side by side, making it easily available and a requirement. It also makes it simple for its consumers to use.
- • While Tableau CRM does provide a few of its data connectors, its main purpose is to analyze Salesforce data. To maximize sales, customer service, and any Salesforce-specific features, it is used. Therefore, any live opportunities, cases, accounts, and any other live data in Salesforce can be put in alongside the insights and visualizations from Tableau CRM to assist those Salesforce users in making choices in real-time.
- • If you require an end-to-end analytics platform for numerous enterprise use cases outside of Salesforce, try Tableau. Choose Tableau if you require business intelligence for the entire firm in addition to Salesforce’s features.
- • The best option, however, for your Salesforce users is Tableau CRM. You will receive fully integrated insights that span the entirety of your Salesforce software, enhancing Sales Cloud, Financial Services Cloud, or Health Cloud to promote increased productivity and data-driven decision-making.
Tableau 2022.3 new features
- Data Guide
- Data Guide is a new pane that gives useful information about a dashboard and the data underlying it. This feature makes it even simpler to locate data that is pertinent to the chosen viz, dashboard, markings, and significant information like outliers and trends in your data.
- Tableau Prep
- We added over 400 problem codes and updated error messages to provide clarity on root causes and troubleshooting actions to help you troubleshoot flow run failures with Tableau Prep Conductor.
- Web data connector 3.0
- Web Data Connector 3.0 offers a toolkit that includes every component required to create a connector in a single file.
- Table extension
- Complete data tables can be dynamically imported into Tableau’s data model from analytics programs like Python, R, Einstein Discovery, and others.
- Advanced analytics and forecasts can be injected into Table Extensions at various levels of detail, and you can obtain real-time data that are dynamically updated.
- Dynamic zone visibility
- You can customize end-user experiences so they only see the dashboard components pertinent to them by using dynamic zone visibility.
Activity log in Tableau server - By exposing more events from your Tableau environment, the Activity Log goes above and beyond the existing event data. These incidents will be organized and recorded.
- Tools and External service support
- Customers using Advanced Management for Tableau Server that uses the Resource Monitoring Tool now have the option of running the PostgreSQL and RabbitMQ services that enable the Resource Monitoring Tool Server outside of AWS using PostgreSQL via AWS RDS and RabbitMQ via AWS AMQ.
Tableau's advantages for data visualization
- User-friendliness: With this tool, anything is possible without any prior knowledge or expertise.
- A tool that works well in the data-driven world: You may use Tableau to link to many data sources, data warehouses, big data, and files that are already present in the cloud while using appealing visuals.
- Convenient use: By using common fields, the user can add new data sets that are immediately integrated with the existing ones. A database or an Excel worksheet is the subject of this.
- Adoptable: Users can choose between different visualizations to delve deeply into the data.
- Tableau tool applications: To make the following process of data visualization quick and easy, Tableau can be used.
- Better data analysis for an organization: Tableau’s straightforward functionalities are used to build insights from Big Data.
- Data warehousing goal: Tableau is the key concept for DWH, which includes dimensional data modeling and BI tools.
- Applications for business intelligence (BI): Tableau is a key player in BI concepts for the creation of ad hoc queries, standardized reporting, analytic applications, dashboard features with a variety of built-in templates, and navigation frameworks.
- Fast and Simple Reporting: Tableau Desktop, Server, Online, Reader, and prep builder all work together to provide customers with quick results.
Advanced benefits at BTree
Interview Preparation
Our placement team supports in interview preparation process and will also help you with technical readiness with access to questions material.
Resume Buliding
BTree has created and re-write more than 300+ job-winning resumes and job cover letters for our learners at no additional cost driven by the course fees.
Recently Placed Candidates
The trainer I received was excellent. The examples he gave seemed more like actual practice, and the hands-on activities in the sessions are fantastic and challenging. The explanations are concise, understandable, and straightforward. When you have a problem, their support staff quickly responds and assists you in resolving it. In my opinion, Btree is the best place to learn because everyone is so friendly and every inquiry gets the proper answer.
I’m completely new to the field of data analytics. I never anticipated that I would pick things up at such a rapid pace. Mastering the Tableau application was greatly aided by the updated curriculum and blended learning. They also provided me with placement assistance. Btree is generally a fantastic option for Tableau training.
I’d like to thank Btree for giving me this opportunity. I worked for Ranet4u. I had no idea how the IT sector operated when I first started working here, but I’m now pleased to identify as a Data Scientist. The technical staff genuinely supports me and helps me improve professionally.
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FAQ on Tableau for Data Science
Can I meet the trainers before enrolling in the course?
- Before enrolling in the certification program, we always encourage our students to meet the trainers for discussion. Only if you are happy with the mentorship provided by the trainer during the initial meeting do we consider you to be a class member.
What if I miss the session?
- Each Data Science and Tableau lesson is recorded so that it can be reviewed as needed before the next session. You get access to all or any classes for 90 days with Flexi-pass, giving you the freedom to choose sessions as you see fit.
Is this Tableau for data science certification course suitable for freshers?
- Yes, Tableau for data science certification training in Chennai is appropriate for beginners. This course covers Tableau Desktop 10, a platform for data visualization, reporting, and business intelligence that is used all over the world.
Does Tableau for data science require coding?
- Yes, Tableau for data science requires one to know about python programing language.
Will I get the certification after completing the Tableau for data science course?
- Each student who completes this Tableau for data science course receives a certificate from Btree Systems.
Is Tableau for data science good for career growth in the analysis domain?
- Learning Tableau for data science will enable you to demonstrate your visualization abilities to potential employers. Data visualization is a critical component of the overall data science lifecycle. As a result, if you’re looking to pursue a career in data analytics, this course will help you become more proficient in this field and put you ahead of your rivals.
Do you provide a career guidance for freshers?
- Yes, we provide a career guidance camp for freshers and working professionals (IT or Non-IT).
What are the different modes of training at BTree Systems provide?
- BTree Systems offers a wide range of instruction to pupils, including
- Classroom instruction
- Individualized instruction
- Online with a live training
- Customized instruction
Where can I book a free demo session?
- You can call us at +91-7397396665, and we’ll get back to you as soon as we can, quickly to learn more about the offers and discounts.
What are the prerequisites for Tableau for data science course?
- There are no prerequisites for Tableau for data science certification course.
What are the payment options we provide?
- Any of the methods listed below may be used to make payments, and for both in-person online and offline training, an email receipt will be immediately sent. We recently offered EMI alternatives to all of our courses.
- EMI option for Debit/Credit Card
- The MasterCard.
- Online banking and PayPal
- Paytm, PhonePe, and Google Pay.
BTree Students Reviews
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Testimonial Reviews
S
Swetha
I completed my Tableau for data science course at Btree Systems. The instructor kept the class engaging with clear explanations and practical examples. now I work for a respectable company as a certified Tableau developer. I’m very grateful to my trainer.
R
Ramya
A truly amazing environment to grow and learn. Trainers are friendly and supportive at all times. In my first attempt, I was placed and certified. They are accommodating and prepared to assist you with every part of the course, including soft skills and personality development. I finished Tableau, had a good experience and learned all I needed to know about the fundamentals. I feel fortunate to have made the proper technological decision, and my sincere gratitude goes out to the trainers.
R
Rakul
Google reviews led me to learn about Btree Systems Tableau training. The training session starts on the first day, and the trainer is quite knowledgeable about the tools. The final training session involves working out practical sessions. Having career guidance and job assistance support was beneficial. Only with the help of the team was it possible for me to complete my certification, and I’m delighted to report that I now work for an MNC as a Tableau developer. grateful always.
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