Data Analytics Training by Experts
Our Training Process
Data Analytics - Syllabus, Fees & Duration
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Learn Python Program from Scratch
- Basic programming concepts
- Object -oriented programming
- Data types, variables, strings, loops, and functions
- Software engineering using Python.
-
Statistical and Mathematical Essential for Data Science
- Collection, classification, and analysis of data
- A foundational part of Data Science
- Explain measures of central tendency and dispersion
- comprehend skewness, correlation, regression, distribution
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Data Science with Python
- Jupyter Notebook and PyCharm based lab environment
- Machine Learning
- Data visualization
- Web scraping
- Natural language processing
-
Database
-
Machine Learning
- Mathematical and heuristic aspects
- Hands-on modeling to develop algorithms
- Advanced Machine Learning knowledge.
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Data Analytics with R:
- Data wrangling
- data exploration
- data visualization
- predictive analytics
- descriptive analytics techniques
- import and export data in R
- data structures in R
- various statistical concepts
- cluster analysis
- forecasting
-
Visualization with Tableau
- Data Visualization
- combo charts
- working with filters
- parameters
- sets
- building interactive dashboards
-
Visualization with Power BI
- Data filtering
- Data manipulations
- understanding the patterns in data
- create customized dashboards with powerful developer tools
Technologies Training:
-
Python:
- Introduction to Python and Computer Programming
- Data Types
- Variables
- Basic Input -Output Operations
- Basic Operators
- Boolean Values
- Conditional Execution
- Loops
- Lists and List Processing
- Logical and Bitwise Operations
- Functions
- Tuples
- Dictionaries
- Sets
- Data Processing
- Modules
- Packages
- String and List Methods
- Exceptions
- File Handlings li> Regular expressions
- the Object - Oriented Approach: Classes, Methods, Objects
- Standard Objective Features; Exception Handling
- Working with Files
R:
- R Introduction
- Data Inputting in R
- Strings
- Vectors
- Lists
- Matrices
- Arrays Functions and Programming in R
- Data manipulation in R
- Factors
- DataFrame
- Packages
- Data Shaping
- R-Data Interface
- Web Data and Database
- Charts-Pie
- Bar Charts
- Boxplots, Histograms
- LineGraphs
- Mean
- Median
- Mode
- Regression-Linear
- Multiple
- Logistic
- Poisson
- Distribution-Normal
- Binomial
- Analysis-Covariance
- Time Series, Survival
- Nonlinear Least Square
- Decision Tree
- Random Forestc
MySQL
- MySQL – Introduction
- Installation
- Create Database
- Drop Database
- Selecting Database
- Data Types
- Create Tables
- Drop Tables
- Insert Query
- Select Query
- WHERE Clause
- Update Query
- DELETE Query
- LIKE Clause
- Sorting Results
- Using Joins
- Handling NULL Values
- ALTER Command
- Aggregate functions
- MySQL Clauses
- MySQL Conditions
Matplotlib:
- Scatter plot
- Bar charts
- histogram
- Stack charts
- Legend title Style
- Figures and subplots
- Plotting function in pandas
- Labelling and arranging figures
- Save plots.
Seaborn:
- Style functions
- Color palettes
- Distribution plots
- Categorical plots
- Regression plots
- Axis grid objects.
NumPy
- Creating NumPy arrays
- Indexing and slicing in NumPy
- Downloading and parsing data Creating multidimensional arrays
- NumPy Data types
- Array attributes
- Indexing and Slicing
- Creating array views copies
- Manipulating array shapes I/O.
Pandas:
- Using multilevel series
- Series and Data Frames
- Grouping
- aggregating
- Merge Data Frames
- Generate summary tables
- Group data into logical pieces
- manipulate dates
- Creating metrics for analysis
- Data wrangling
- Merging and joining
- Data Mugging using Pandas
- Building a Predictive Mode.
Scikit-learn:
- Scikit Learn Overview
- Plotting a graph
- Identifying features and labels
- Saving and opening a model
- Classification
- Train / test split
- What is KNN? What is SVM?
- Linear regression
- Logistic vs linear regression
- KMeans
- Neural networks
- Overfitting and underfitting
- Backpropagation
- Cost function and gradient descent, CNNs
Tableau
- Tableau Architecture
- File Types
- Data Types
- Tableau Operator
- String Functions
- Date Functions Logical Functions
- Aggregate FunctionsvJoins in Tableau
- Types of Tableau Data Source
- Data Extracts
- Filters
- Sorting
- Formatting
- Adding Worksheets and Renaming Worksheet In Tableau
- Tableau Save
- Reorder and Delete Worksheet
- Charts
- dashboard.
Power BI
- Power BI Architecture
- Components
- Power BI Desktop
- Connect to Data in Power BI Desktop
- Data Sources for Power BI
- DAX in Power BI
- Q & A in Power BI
- Filters in Power BI, Power BI Query Overview
- Creating and Using Measures in Power
- Calculated Columns
- Data Visualizations
- Charts
- Area
- Funnel
- Combo
- Donut
- Waterfall
- Line
- Maps
- Bar
- KPI
- Power BI Dashboard
This syllabus is not final and can be customized as per needs/updates
- Since 2001, Making IT Experts by Experts
- Duration: 70 Hours (Vary as per your skill)
- Course Fees
- Individual Live Classes (1:1)
- Batch Classes @ Low Fees
- Training on Your Time, Any Where
- Access to Recorded Videos
- Practical Internship on Projects
- 100% Placement Support by our jobsNEAR.in
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Course Highlights
Featured Courses
Data analytics training involves acquiring the knowledge and skills needed to analyze and interpret data to make informed business decisions. Work on real-world projects, participate in online competitions (such as Kaggle), and continue learning to enhance your skills. A data analytics course is an educational program designed to teach individuals the skills and knowledge needed to work in the field of data analytics. Here are some common components of a data analytics course:. These courses are offered by various educational institutions, including universities, online platforms, and specialized training providers. The content of data analytics courses can vary, but they typically cover a range of topics related to collecting, analyzing, and interpreting data to extract valuable insights. Here is a step-by-step guide to help you get started with data analytics training: Remember that practice is essential in data analytics.
