Data Analytics Training in Chandigarh

DATA ANALYTICS PROGRAM

Data analytics, also known as data analysis, is a crucial component of modern business operations. It involves examining datasets to uncover useful information that can be used to make informed decisions. This process is used across industries to optimize performance, improve decision-making, and gain a competitive edge.

A good training in data analytics will help a person to get complete core and advanced knowledge by working on real time projects. Only a well reputed company can deliver this power packed performance. If you are trying to acquire your dream job, then TCIL-IT Chandigarh will be your prime destination to equip yourself with all the advanced versions of data analytics training in Chandigarh. TCIL-IT Chandigarh is a leading training company in Chandigarh which acts as valuable source of knowledge for all training courses related to information technology and telecommunication services.

With us, you will get a golden opportunity to work with famous data analysts who can teach you various key features of handling various rough and tough situations in live projects. Being new developers, all the mistakes made by you will get clarified and rectified. Our trainers are well recognized among various industries. You will get exposed to all the latest modules that supplement creative features in data analytics. With us, you can sharpen your professional skill along with other soft skills that are essential in the field of recruitment. We us, you can boost up your confidence and perform excellently in any interview to attain your dream job.

 

DATA ANALYTICS WITH PYTHON

1. Introduction to Data Analytics
  • 1.1 Definition of data and information
  • 1.2 Types of data
    • Structured data
    • Unstructured data
  • 1.3 Meaning and scope of data analytics
  • 1.4 Difference between data analytics, data science, and business analytics
  • 1.5 Types of analytics
    • Descriptive analytics
    • Diagnostic analytics
    • Predictive analytics
    • Prescriptive analytics
  • 1.6 Data analytics lifecycle
    • Business problem understanding
    • Data collection from multiple sources
    • Data cleaning and preprocessing
    • Exploratory data analysis
    • Data visualization and reporting
    • Insight generation and decision making
  • 1.7 Overview of tools used in data analytics
    • Microsoft Excel
    • MySQL
    • Python
    • Power BI
2. Data Analysis Using Excel
  • 2.1 Introduction to Microsoft Excel
    • Excel interface and workbook structure
    • Worksheets, rows, columns, and cells
    • Data types in Excel
  • 2.2 Data importing techniques
    • Importing CSV files
    • Importing Excel files
    • Copying data from external sources
  • 2.3 Data cleaning in Excel
    • Removing duplicate records
    • Handling missing and blank values
    • Find and replace operations
    • Text to columns
    • Flash fill
    • Data validation rules
  • 2.4 Excel formulas and functions
    • Arithmetic functions
    • Logical functions such as IF, AND, OR
    • Lookup and reference functions
    • VLOOKUP and HLOOKUP
    • XLOOKUP
    • COUNTIF, SUMIF, AVERAGEIF
    • Text functions
    • Date and time functions
  • 2.5 Data analysis tools
    • Sorting and filtering data
    • Conditional formatting
    • Pivot tables
    • Pivot charts
  • 2.6 Visualization and dashboards
    • Creating charts such as bar, line, column, and pie
    • Designing dashboards
    • KPI indicators
    • Slicers and interactive controls
  • 2.7 Practical use cases
    • Sales performance analysis
    • Student result analysis
    • Monthly revenue reports
3. Database and Data Analysis Using MySQL
  • 3.1 Introduction to databases
    • Concept of databases
    • DBMS and RDBMS
    • Importance of databases in analytics
  • 3.2 MySQL fundamentals
    • Database creation
    • Table creation
    • Primary key and foreign key
    • Data types
  • 3.3 Basic SQL commands
    • SELECT statement
    • WHERE clause
    • DISTINCT keyword
    • ORDER BY clause
    • LIMIT clause
  • 3.4 Aggregate functions
    • COUNT
    • SUM
    • AVG
    • MIN and MAX
  • 3.5 Grouping and filtering data
    • GROUP BY clause
    • HAVING clause
  • 3.6 SQL joins
    • INNER JOIN
    • LEFT JOIN
    • RIGHT JOIN
    • Concept of FULL JOIN
    • Self join
  • 3.7 Advanced SQL concepts
    • Subqueries
    • Nested queries
    • CASE statements
    • Views
    • Indexes
    • Date functions
    • String functions
  • 3.8 Data cleaning using SQL
    • Handling NULL values
    • Removing duplicate records
    • Data type conversion
  • 3.9 Practical use cases
    • Customer purchase analysis
    • Sales trend analysis
    • Product performance analysis
4. Python for Data Analytics
  • 4.1 Introduction to Python
    • Python features and advantages
    • Installation and setup
    • Jupyter Notebook overview
  • 4.2 Python basics
    • Variables and data types
    • Operators
    • Input and output
  • 4.3 Control structures
    • Conditional statements
    • Looping statements
    • Functions
    • Lambda expressions
  • 4.4 Python data structures
    • Lists
    • Tuples
    • Sets
    • Dictionaries
  • 4.5 File handling
    • Reading CSV files
    • Reading Excel files
    • Writing data to files
  • 4.6 Practical applications
    • Loading datasets
    • Basic data exploration
    • Data type inspection
5. NumPy for Numerical Data Processing
  • 5.1 Introduction to NumPy
    • Importance of numerical computing
    • NumPy arrays
  • 5.2 Array creation and properties
    • One-dimensional and multi-dimensional arrays
    • Shape and size
    • Data types
  • 5.3 Array operations
    • Indexing and slicing
    • Reshaping arrays
    • Mathematical operations
    • Statistical functions
  • 5.4 Advanced NumPy concepts
    • Broadcasting
    • Vectorization
    • Performance optimization
    • Comparison between NumPy arrays and Python lists
  • 5.5 Practical applications
    • Numerical analysis on sales data
    • Statistical computations
6. Pandas for Data Manipulation and Analysis
  • 6.1 Introduction to Pandas
    • Series and DataFrame
    • Reading data from CSV, Excel, and databases
  • 6.2 Data inspection techniques
    • Head and tail functions
    • Info and describe functions
  • 6.3 Data cleaning
    • Handling missing values
    • Dropping and filling data
    • Removing duplicates
    • Data type conversion
  • 6.4 Data analysis operations
    • Filtering and sorting data
    • GroupBy operations
    • Aggregation functions
    • Apply and lambda functions
  • 6.5 Data transformation
    • Merging and joining DataFrames
    • Concatenation
    • Feature engineering
  • 6.6 Practical applications
    • E-commerce data analysis
    • Customer segmentation
    • Sales performance evaluation
7. Data Visualization Using Matplotlib and Seaborn
  • 7.1 Introduction to data visualization
    • Importance of visualization
    • Types of charts
  • 7.2 Matplotlib
    • Line charts
    • Bar charts
    • Pie charts
    • Histograms
    • Scatter plots
    • Plot customization
  • 7.3 Seaborn
    • Count plots
    • Box plots
    • Violin plots
    • Heatmaps
    • Pair plots
    • Correlation analysis
  • 7.4 Visualization best practices
    • Choosing the correct chart
    • Data storytelling principles
  • 7.5 Practical applications
    • Trend analysis
    • Customer behavior visualization
8. Power BI for Business Intelligence
  • 8.1 Introduction to Power BI
    • Power BI Desktop overview
    • Connecting data sources
  • 8.2 Data transformation
    • Power Query Editor
    • Data cleaning and shaping
  • 8.3 Data modeling
    • Relationships
    • Cardinality
    • Star schema concept
  • 8.4 DAX fundamentals
    • Calculated columns
    • Measures
    • Common DAX functions
    • Time intelligence
  • 8.5 Reports and dashboards
    • Creating visuals
    • Filters and slicers
    • KPI cards
    • Publishing and sharing reports
  • 8.6 Practical applications
    • Sales dashboard
    • Business performance dashboard
9. End-to-End Data Analytics Project
  • 9.1 Problem definition
  • 9.2 Data collection
  • 9.3 Data cleaning using Excel, SQL, and Python
  • 9.4 Exploratory data analysis
  • 9.5 Visualization using Python
  • 9.6 Dashboard creation using Power BI
  • 9.7 Insight generation and presentation