Data Analytics Training in Kannur

1. Learn Python Program from Scratch

   • Basic programming concepts
   • Object -oriented programming
   • Data types, variables, strings, loops, and functions
   • Software engineering using Python.

2. 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

3. Data Science with Python

   • Jupyter Notebook and PyCharm based lab environment
   • Machine Learning
   • Data visualization
   • Web scraping
   • Natural language processing

4. Database

5. Machine Learning

   • Mathematical and heuristic aspects
   • Hands-on modeling to develop algorithms
   • Advanced Machine Learning knowledge.

6. 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

7. Visualization with Tableau

   • Data Visualization
   • combo charts
   • working with filters
   • parameters
   • sets
   • building interactive dashboards

8. 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