Data Analytics is used for Analyzing customer behavior, forecasting future trends and preferences to tailor marketing strategies and improve customer satisfaction and many more uses still exist which is why Data Analytics are in-demand currently. Our Data Analytics Training Institute comes with the most up-to-date syllabus and modern infrastructure, along with experienced trainers as well. Therefore, our Data Analytics Course will give students a holistic learning of Data Analytics, which will eventually give them a prolonged, high-paying career in Data Analytics as Data Analyst and so on. So go ahead and explore more down below to get all the information you need about our Data Analytics Course with certification & placements.
Data Analytics Training
DURATION
2 Months
Mode
Live Online / Offline
EMI
0% Interest
Let's take the first step to becoming an expert in Data Analytics Training
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What this Course Includes?
- Technology Training
- Aptitude Training
- Learn to Code (Codeathon)
- Real Time Projects
- Learn to Crack Interviews
- Panel Mock Interview
- Unlimited Interviews
- Life Long Placement Support
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Course Schedules
Course Syllabus
Course Fees
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Breakdown of Data Analytics Training Fee and Batches
Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
March 2025
Week days
(Mon-Fri)
Online/Offline
2 Hours Real Time Interactive Technical Training
1 Hour Aptitude
1 Hour Communication & Soft Skills
(Suitable for Fresh Jobseekers / Non IT to IT transition)
March 2025
Week ends
(Sat-Sun)
Online/Offline
4 Hours Real Time Interactive Technical Training
(Suitable for working IT Professionals)
Save up to 20% in your Course Fee on our Job Seeker Course Series
Syllabus of Data Analytics Training
CORE PYTHON
- Python Introduction & history
- Color coding schemes
- Salient features & flavors
- Application types
- Language components
- String handling management
- String operations – indexing, slicing, ranging
- String methods – concatenation, repetition, formatting
- Supporting functions
- Native data types
- List
- Tuple
- Set
- Dictionary
- Decision making statements
- If
- If…else
- If…elif…else
- Looping statements
- For loop
- While loop
- Function types
- Built-in functions
- Math functions
- User defined functions
- Recursive functions
- Lambda functions
- OOPs
- Classes and objects
- init constructor
- Self-keyword
- Data abstraction
- Data encapsulation
- Polymorphism
- Inheritance
- Exception handling
- Error vs exception
- Types of error
- User defined exception handling
- Exception handler components
- Try block, except block, finally block
POWER BI INTRODUCTION
- Data Visualization
- Reporting Business Intelligence (BI)
- Traditional BI
- Self-Serviced BI Cloud Based BI
- On Premise BI
- Power BI Products
- Power BI Desktop (Power Query, Power Pivot, Power View)
- Flow of Work in Power BI Desktop
- Power BI Report Server
- Power BI Service, Power BI Mobile
- Power BI Architecture
- A Brief History of Power BI
POWER QUERY
- Data Transformation
- Benefits of Data Transformation
- Shape or Transform Data using Power Query
- Overview of Power Query / Query Editor
- Query Editor User Interface
- The Ribbon (Home, Transform, Add Column, View Tabs)
- The Queries Pane
- The Data View / Results Pane
- The Query Settings Pane, Formula
- Bar Saving the Work
- Data types
- Changing the Data type of a Column Filters in Power Query
- Auto Filter / Basic Filtering Filter a Column using
- Text Filters Filter a Column using Number Filters
- Filter a Column using Date Filters Filter Multiple Columns
- Remove Columns / Remove Other Columns Name
- Rename a Column Reorder Columns or Sort Columns
- Add Column / Custom Column Split Columns Merge
- Columns PIVOT, UNPIVOT Columns Transpose Columns
- Header Row or Use First Row as Headers Keep Top Rows
- Keep Bottom Rows Keep Range of Rows Keep Duplicates
- Keep Errors Remove Top Rows
- Remove Bottom Rows
- Remove Alternative Rows
- Remove Duplicates, Remove Blank Rows
- Remove Errors Group Rows / Group By
M LANGUAGE
- IF..ELSE Conditions
- TransformColumn()
- RemoveColumns()
- SplitColumns()
- ReplaceValue()
- Table.Distinct() Options and GROUP BY Options
- Table.Group()
- Table.Sort() with Type Conversions
- PIVOT Operation and Table.Pivot ().
- List Functions Using Parameters with M Language
DATA MODELING
- Data Modeling Introduction Relationship
- Need of Relationship Relationship Types
- Cardinality in General
- One-to-One
- One-to-Many
- Many-to-One
- Many-to-Many
- AutoDetect the relationship
- Create a new relationship
- Edit existing relationships
- Make Relationship Active or Inactive
- Delete a relationship
DAX
- What is DAX
- Calculated Column, Measures
- DAX Table and Column Name Syntax
- Creating Calculated Columns
- Creating Measures
- Calculated Columns Vs Measures
- DAX Syntax & Operators
- Types of Operators
- Arithmetic Operators
- Comparison Operators
- Text Concatenation Operator
- Logical Operators
DAX FUNCTIONS TYPES
- Date and Time Functions
- YEAR, MONTH,DAY
- WEEKDAY, WEEKNUM FORMAT (Text Function)
- Month Name, Weekday Name
- IF
- TRUE, FALSE NOT,
- OR, IN, AND
- Text Function
- LEN, CONCATENATE
- LEFT, RIGHT, MID UPPER
- LOWER TRIM, SUBSTITUTE, BLANK
- Logical Functions
- IF TRUE, FALSE NOT
- OR, IN, AND IF ERROR SWITCH
- Math & Statistical Functions
- INT ROUND, ROUNDUP
- ROUNDDOWN
- DIVIDE EVEN, ODD
- POWER, SIGN SQRT
- FACT SUM, SUMX MIN, MINX MAX
- MAXX COUNT,
- COUNTX AVERAGE
- AVERAGEX COUNTROWS
- COUNTBLANK
REPORT VIEW
- Report View User Interface
- Fields Pane
- Visualizations pane
- Ribbon, Views, Pages Tab
- Canvas Visual Interactions Interaction Type (Filter, Highlight, None)
- Visual Interactions Default Behavior, Changing the Interaction
- Grouping and Binning Introduction
- Using grouping, Creating Groups on Text Columns
- Using binning, Creating Bins on Number Column and Date Columns
- Sorting Data in Visuals
- Changing the Sort Column
- Changing the Sort Order
- Sort using column that is not used in the Visualization
- Sort using the Sort by Column button
- Hierarchy Introduction
- Default Date Hierarchy
- Creating Hierarchy
- Creating Custom Date Hierarchy
- REPORT VIEW
- Change Hierarchy Levels
- Drill-Up and Drill-Down Reports
- Data Actions, Drill Down, Drill Up, Show Next Level
- Expand Next Level Drilling filters other visuals option
VISUALIZATIONS
- Visualizing Data
- Why Visualizations
- Visualization types
- Create and Format Bar and Column Charts
- Create and Format Stacked Bar Chart
- Stacked Column Chart
- Create and Format Clustered Bar Chart
- Clustered Column Chart
- Create and Format 100% Stacked Bar Chart 100% Stacked Column Chart
- Create and Format Pie and Donut Charts
- Create and Format Scatter Charts
- Create and Format Table Visual
- Matrix Visualization
- Line and Area Charts
- Create and Format Line Chart, Area Chart
- Stacked Area Chart Combo Charts
- VISUALIZATIONS
- Create and Format Line and Stacked Column Chart
- Line and Clustered Column Chart
- Create and Format Ribbon Chart
- Waterfall Chart, Funnel Chart
POWER BI SERVICE
- Power BI Service Introduction
- Power BI Cloud Architecture
- Creating Power BI Service Account
- SIGN IN to Power BI Service Account
- Publishing Reports to the Power BI service
- Import / Getting the Report to PBI Service
- My Workspace / App Workspaces Tabs
- DATASETS, WORKBOOKS, REPORTS & DASHBOARDS
- Working with Datasets Creating Reports in Cloud using Published
- Datasets
- Creating Dashboards Pin Visuals and Pin LIVE
- Report Pages to Dashboard
- Advantages of Dashboards Interacting with
- Dashboards
- Formatting Dashboard, Sharing Dashboard
ADVANCED PANDAS FUNCTIONS
- Group by()
- Pivot tables()
- Multi-indexing()
- merge()
- concatenate()
- join()
- data transformation using apply()
- map()
- query()
- Resampling time series functionality
- excel writer()
- pipe()
- creating dataframes
- reading CSV files with intrinsic index
- converting CSV files to dataframes
- converting dataframes to CSV files
- converting dataframes to excel file
ADVANCED SQL FUNCTIONS
- Common Table Expressions (CTE)
- Recursive CTE’s
- temporary functions
- pivoting data with sum() and CASE WHEN
- Except vs Not in
- self joins, rank vs dense_rank vs row number
- ranking data
- calculating delta values,
- multiple groupings using rollup
- calculating running totals
- computing a moving average
- date time manipulations
- Formatting strings, stored methods
- JOINS
- Sub Queries
- Manipulation of date and time
- procedural data storage
- Connecting SQL to Python or R language, window Functions
PROJECT
- Project1 – Product Sales Analysis – Power BI Project and review
- Project2 – Financial Performance Analysis – Power BI Project and review
- Project3 – Health care sales Analysis –
- Intermediate Power BI project and review
- Project4 – Anamoly detection in Credit card transactions – Intermediate Power BI project and review
Objectives of Learning Data Analytics Training
The Data Analytics Training will cover all the topics ranging from fundamental to advanced concepts, which will make it easy for students to grasp Data Analytics. The Data Analytics Course Curriculum is composed of some of the most useful and rare concepts that will surely give students a complete understanding of Data Analytics as well. So, some of those curriculum are discussed below as objectives:
- To make students well-versed with fundamental concepts in Data Analytics like – Core Python, Power BI, Power Query, M Language, Data Modeling etc.
- To make students know more about Data Analytics by exploring topics like – Visualization, Power BI Service, Advanced Pandas Function etc.
- To make students knowledgeable in advanced concepts in Data Analytics by making them learn topics like – Advanced SQL Functions – Common Table Expressions (CTE), Recursive CTE’s, temporary functions, pivoting data with sum() and CASE WHEN, Except vs Not in etc.
Reason to choose SLA for Data Analytics Training
- SLA stands out as the Exclusive Authorized Training and Testing partner in Tamil Nadu for leading tech giants including IBM, Microsoft, Cisco, Adobe, Autodesk, Meta, Apple, Tally, PMI, Unity, Intuit, IC3, ITS, ESB, and CSB ensuring globally recognized certification.
- Learn directly from a diverse team of 100+ real-time developers as trainers providing practical, hands-on experience.
- Instructor led Online and Offline Training. No recorded sessions.
- Gain practical Technology Training through Real-Time Projects.
- Best state of the art Infrastructure.
- Develop essential Aptitude, Communication skills, Soft skills, and Interview techniques alongside Technical Training.
- In addition to Monday to Friday Technical Training, Saturday sessions are arranged for Interview based assessments and exclusive doubt clarification.
- Engage in Codeathon events for live project experiences, gaining exposure to real-world IT environments.
- Placement Training on Resume building, LinkedIn profile creation and creating GitHub project Portfolios to become Job ready.
- Attend insightful Guest Lectures by IT industry experts, enriching your understanding of the field.
- Panel Mock Interviews
- Enjoy genuine placement support at no cost. No backdoor jobs at SLA.
- Unlimited Interview opportunities until you get placed.
- 1000+ hiring partners.
- Enjoy Lifelong placement support at no cost.
- SLA is the only training company having distinguished placement reviews on Google ensuring credibility and reliability.
- Enjoy affordable fees with 0% EMI options making quality training affordable to all.
Highlights of The Data Analytics Training
What is Data Analytics?
Data Analytics is the process of systematically analyzing data to identify patterns, trends, and insights that guide decision-making. It utilizes methods like statistical analysis, data mining, and machine learning to interpret and visualize data, enabling organizations to make well-informed decisions and address complex challenges.
What is Data Analytics Full Stack?
Data Analytics Full Stack covers all stages of the analytics workflow, including data collection, cleaning, analysis, and visualization. It involves managing data storage, applying advanced analytics like machine learning, and deploying solutions into business applications. Mastering these components ensures a complete approach to data-driven decision-making.
What are the reasons for learning Data Analytics?
The following are the prerequisites for learning Data Analytics:
- Enhanced Decision-Making: Data analytics delivers actionable insights that facilitate informed decisions across various industries.
- Diverse Career Paths: Mastery in data analytics opens up a variety of job roles in fields like finance, healthcare, technology, and marketing.
- Complex Problem-Solving: By examining data, you can identify and address complex issues through trend and pattern recognition.
- Operational Efficiency: Data analytics helps optimize processes and operations, leading to increased efficiency and reduced costs
What are the prerequisites for learning Data Analytics?
The following are the prerequisites for learning Data Analytics:
- Mathematics and Statistics: Basic knowledge of mathematics and statistics, including concepts like descriptive statistics, probability, and inferential statistics, is essential.
- Spreadsheet Skills: Proficiency in spreadsheet tools such as Microsoft Excel or Google Sheets for data manipulation, analysis, and visualization.
- Database Fundamentals: Understanding database principles and basic SQL (Structured Query Language) for querying and managing data.
- Programming Knowledge: Familiarity with programming languages like Python or R, commonly used for data analysis tasks.
What are the course fees and duration?
Our Data Analytics Course Fees may vary depending on the specific course program you choose (basic / intermediate / full stack), course duration, and course format (remote or in-person). On an average the Data Analytics Course Fees range from 55k to 60k, for a duration of 3 months with international certification based on the above factors.
What are some of the jobs related to Data Analytics?
The following are the jobs related to Data Analytics:
- Data Analyst
- Data Scientist
- Business Intelligence (BI) Analyst
- Data Consultant
- Quantitative Analyst
- Data Engineer
List a few real time Data Analytics applications.
The following are the Data Analytics real-time applications:
- Fraud Detection
- Stock Market Analysis
- Customer Experience Management
- Network Security
- Supply Chain Management
- Healthcare Monitoring
Who are our Trainers for Data Analytics Training?
Our Mentors are from Top Companies like:
- Our Data Analytics instructors are highly skilled professionals with extensive technical knowledge and expertise in analytics. They bring significant experience in managing and executing data-driven solutions.
- They develop thorough study materials to assist learners in mastering the fundamentals of data analytics and exploring various analytical fields.
- Our trainers have an in-depth understanding of data analytics certification and guide students through concepts such as data exploration, machine learning algorithms, Big Data fundamentals, and data visualization.
- They excel in preparing students for certification exams by employing real-world scenarios and mentoring techniques. Their excellent communication and interpersonal skills facilitate effective instruction and student engagement.
- They are capable of deploying secure, high-performance data solutions and configuring servers for advanced computing tasks.
- Additionally, they can develop and deploy applications throughout the software development life cycle. By using analytics platforms and techniques, they evaluate student performance and offer constructive feedback to enhance their analytics skills.
- Their collaborative approach and enthusiasm help students gain the necessary knowledge and achieve their career goals effectively.
What Modes of Training are available for Data Analytics Training?
Offline / Classroom Training
- Direct Interaction with the Trainer
- Clarify doubts then and there
- Airconditioned Premium Classrooms and Lab with all amenities
- Codeathon Practices
- Direct Aptitude Training
- Live Interview Skills Training
- Direct Panel Mock Interviews
- Campus Drives
- 100% Placement Support
Online Training
- No Recorded Sessions
- Live Virtual Interaction with the Trainer
- Clarify doubts then and there virtually
- Live Virtual Interview Skills Training
- Live Virtual Aptitude Training
- Online Panel Mock Interviews
- 100% Placement Support
Corporate Training
- Industry endorsed Skilled Faculties
- Flexible Pricing Options
- Customized Syllabus
- 12X6 Assistance and Support
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Project Practices for Data Analytics Training
Supply Chain Analysis
Evaluate supply chain data to find inefficiencies, optimize inventory levels, and enhance logistics. Apply data-driven insights to improve overall supply chain operations.
Fraud Detection
Create models to identify fraudulent activities or anomalies in transaction data. Use statistical methods or machine learning algorithms to detect unusual patterns.
Dashboard Development
Build interactive dashboards using tools like Tableau, Power BI, or Google Data Studio to visualize key metrics and track business performance.
A/B Testing
Design and evaluate A/B tests to determine the impact of different marketing approaches or website modifications. Analyze the results to measure changes in user behavior and performance metrics.
Sentiment Analysis
Analyze customer feedback and social media content to assess public sentiment towards a product or brand. Implement natural language processing (NLP) to process and interpret text data.
Churn Prediction
Develop a model to anticipate customer churn by examining past data and recognizing factors influencing customer retention or loss.
Market Basket Analysis
Analyze transaction data to identify product associations and patterns, employing methods such as association rules and the Apriori algorithm to understand buying behaviors.
Sales Prediction
Create models to predict future sales using historical data, seasonal patterns, and market conditions. Utilize time series analysis or machine learning techniques for precise forecasting.
Customer Segmentation
Examine customer data to categorize segments based on purchasing habits, demographics, or other characteristics. Apply clustering methods to identify and analyze different customer groups.
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Realtime Projects
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Genuine Placements. No Backdoor Jobs at Softlogic Systems.
Free 100% Placement Support
Aptitude Training
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Softskills Training
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Build Your Resume
Build your LinkedIn Profile
Build your GitHub
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Panel Mock Interview
Unlimited Interviews until you get placed
Life Long Placement Support at no cost
FAQs for
Data Analytics Training
How do descriptive, diagnostic, predictive, and prescriptive analytics differ from one another?
1.
Descriptive analytics summarizes past data to provide insights into what happened. Diagnostic analytics investigates why something happened. Predictive analytics estimates future trends using historical data, while prescriptive analytics advises on actions to achieve particular objectives or results.
What methods do you use to address missing data in a dataset?
2.
Missing data can be handled through various methods such as imputation (replacing missing values with statistical estimates), deletion (removing rows or columns with missing data), or using algorithms designed to handle missing values directly.
What are the advantages of using machine learning algorithms in data analysis?
3.
Machine learning algorithms can uncover complex patterns and relationships in large datasets, automate predictive modeling, and adapt to new data over time, providing more accurate and dynamic insights.
What measures do you take to maintain data privacy and security during analysis?
4.
Ensuring data privacy and security involves implementing encryption, access controls, anonymizing sensitive data, and adhering to regulatory standards such as GDPR or HIPAA to protect data throughout the analysis process.
What are some common challenges in data visualization?
5.
Common challenges include choosing the right type of visualization for the data, ensuring clarity and readability, avoiding misleading representations, and effectively communicating insights to diverse audiences.
How do you evaluate the performance of a predictive model?
6.
The performance of a predictive model can be evaluated using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, and mean squared error, depending on the type of model and the nature of the data.
What is the role of SQL in data analytics?
7.
SQL (Structured Query Language) is used for querying, managing, and manipulating relational databases. It allows analysts to retrieve, filter, and aggregate data efficiently, which is essential for performing data analysis.
What is your method for integrating data from various sources?
8.
Data integration is the process of merging data from multiple sources to create a consolidated view. This can be achieved through data cleaning, transformation, and mapping processes, using tools and techniques such as ETL (Extract, Transform, Load) and data warehousing.
Where is the corporate office of the Softlogic Systems located?
9.
The corporate office of the Softlogic Systems is located at the K.K.Nagar.
What payment methods does Softlogic accept?
10.
Softlogic accepts a wide range of payment methods, including:
- Cash
- Debit cards
- Credit cards (MasterCard, Visa, Maestro)
- Net banking
- UPI
- Including EMI
Additional Information for
Data Analytics Training
1.
Scopes available in the future for learning Data Analytics
The following are the scopes available in the future for learning the Data Analytics Course:
- Advanced Machine Learning: Applying advanced machine learning techniques, including deep learning and neural networks, for more precise predictive and prescriptive analytics.
- Big Data Technologies: Gaining expertise in big data tools such as Hadoop and Spark to manage and analyze extensive datasets.
- Real-Time Analytics: Creating systems for real-time data processing and analysis to facilitate prompt decision-making.
- Data Privacy and Ethics: Focusing on data privacy regulations, ethical issues, and best practices for data security.
- Automated Analytics: Using automation tools to optimize data processing and analysis workflows.
- Augmented Analytics: Utilizing augmented analytics tools that leverage AI for enhanced data discovery and insight generation.
- Data Storytelling: Developing skills in data visualization and storytelling to clearly communicate insights to non-technical audiences.
- IoT Data Analysis: Analyzing data from Internet of Things (IoT) devices for various applications in sectors like healthcare, manufacturing, and smart cities.