Data Analytics Training/Course by Experts

1. Learn Python Program from Scratch

   Programming is an increasingly important skill; this program will establish your proficiency in handling basic programming concepts. By the end of this program, you will understand object -oriented programming; basic programming concepts such as data types, variables, strings, loops, and functions; and software engineering using Python.

2. Statistical and Mathematical Essential for Data Science

   Statistics is the science of assigning a probability through the collection, classification, and analysis of data. A foundational part of Data Science, this session will enable you to define statistics and essential terms related to it, explain measures of central tendency and dispersion, and comprehend skewness, correlation, regression, distribution. Understanding the data is the key to perform Exploratory Data analysis and justify your conclusion to the business or scientific problem.

3. Data Science with Python

   Perform fundamental hands-on data analysis using the Jupyter Notebook and PyCharm based lab environment and create your own Data Science projects learn the essential concepts of Python programming and gain in-depth knowledge in data analytics, Machine Learning, data visualization, web scraping, and natural language processing. Python is a required skill for many Data Science positions.

4. Database

   A database is an organized collection of structured information, or data, typically stored electronically in a computer system. A database is usually controlled by a database management system (DBMS). Company data are store in databases and later on retrieved using python to develop analytics and bring insights to business problems.

5. Machine Learning

   It will make you an expert in Machine Learning, a subclass of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge.

6. Data Analytics with R:

   The Data Science with R enables you to take your data science skills to solve multiple problems with statistical and related libraries. The course makes you skilled with data wrangling, data exploration, data visualization, predictive analytics, and descriptive analytics techniques. You will learn about R from basics with installation to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.

7. Visualization with Tableau

   Data Science with Tableau helps to see and understand data solving various business problems. Our visual analytics platform is transforming the way people use data to solve problems. C ourse enables you to create visualizations, organize data, and design plots and develop dashboards to bring more insights to the problem. Learn various concepts of Data Visualization, combo charts, working with filters, parameters, and sets, and building interactive dashboards.

8. Visualization with Power BI

   This Power BI deals with how to handle multiple data sources, extract them perform various data filtering, manipulations, understanding the patterns in data and create customized dashboards with powerful developer tools It is suitable for business intelligence (BI) and reporting professionals, data analysts, and professionals working with data in any sector.

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, and Data Processing, Modules, Packages, String and List Methods, and Exceptions, File Handlings. Regular expressions, the Object - Oriented Approach: Classes, Methods, Objects, and the Standard Objective Features; Exception Handling, and 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 Interfa ce, Web Dataand Database, Charts-Pie, Bar Charts, Boxplots, Histograms, LineGraphs, Mean, Median and Mode, Regression- Linear, Multiple, Logistic, Poisson, Distribution-Normal, Binomial, Analysis-Covariance, Time Series, Survival, Nonlinear Least Square, DecisionTree, 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 Functions, Joins 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 .