Data Analytics Training by Experts

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Our Training Process

Data Analytics - Syllabus, Fees & Duration

  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

Download Syllabus - Data Analytics
  Course Fees    
10000+
20+
50+
25+

Data Analytics Jobs in Toowoomba

Enjoy the demand

Find jobs related to Data Analytics in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Toowoomba, chennai and europe countries. You can find many jobs for freshers related to the job positions in Toowoomba.

  • Data Analyst
  • Business Intelligence Analyst
  • Data Scientist
  • Data Engineer
  • Quantitative Analyst
  • Market Research Analyst
  • Operations Analyst
  • Healthcare Analyst
  • Supply Chain Analyst
  • Fraud Analyst

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Data Analytics Internship/Course Details

Data Analytics internship jobs in Toowoomba
Data Analytics Data analytics training involves acquiring the knowledge and skills needed to analyze and interpret data to make informed business decisions. 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. 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. Here are some common components of a data analytics course:. Work on real-world projects, participate in online competitions (such as Kaggle), and continue learning to enhance your skills.

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List of Training Institutes / Companies in Toowoomba

  • UniversityOfSouthernQueensland(UniSQ) | Location details: UniSQ Toowoomba, 487-535 West St, Darling Heights QLD 4350, Australia | Classification: University, University | Visit Online: unisq.edu.au | Contact Number (Helpline): +61 1800 269 500
courses in Toowoomba
As the transformation process is intertwined with human institutions, a detailed model of the process must consider both structure and institutions; yet many structural models omit institutional factors and this has been considered their greatest weakness (Williamson, 2000). Institutions and perceptions are an important element of transformation (Mwangi, 2006), so it is appropriate that the dynamics of tourism transformation have been frequently investigated using resident perceptions of the industry (Allen, Long, Perdue and Kieselback, 1988; Andereck, Valentine, Knopf and Vogt, 2005; Andriotis, 2005; Ap, 1992; Belisle and Hoy, 1980; Besculides, Lee and McCormick, 2002; Harrill, 200 ; Horn and Simmons, 2002; Johnson, et al. This implies that destinations further progressed in tourism development would be considered less ‘unique’ than a region in which tourism has just commenced. Review of the literature indicates that there is a lack of knowledge surrounding the dynamic interaction of structures and institutions and the reciprocal relationship they have with tourism, particularly at a local level (Agarwal, 2002; Scott, 2003; Rodriguez, Parra-Lopez and Yanes-Estevez, 2008). , 199 ; Perdue, Long and Allen, 1990). These studies have often been undertaken for two primary reasons: to overcome barriers to successful and sustainable tourism development (commonly termed paradoxes) and to provide insight into the level of impact tourism has on the community (Diedrich and Garcia- Baudes, 2009). Indeed, this has been extended to suggest that population perceptions can be indicators of destination decline (Faulkner & Tideswell, 1997; Diedrich & Garcia-Buades, 2009). One occurs when tourists are attracted to the unspoiled nature of a destination, but their increasing visitation transforms the destination and traditional lifestyle into a more urban or globalised one (Bruner, 1991; Dahms and McComb, 1999; Agarwal, 2002; Zhong, et al. This literature shows that the social impact and sensitivity of an area can be measured by monitoring local community perceptions of tourism through 3 social impact or evaluation studies (Fredline, Deery, & Jago, 2005 ; Delamere, 2001; Delamere, Wankel and Hinch, ). Some common paradoxes of transformation are cited within the literature.

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