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Data Science with Python

Data Science with Python

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, structured and unstructured, similar to data mining. We are Data Science SMEs. We provide Data Science solutions & Training to streamline the business information to predict and achieve the best outcomes. This course focus to develop Smart Data Science professionals with powerful tools like python, Tableau, SQL and hadoop that can understand the Data from its inception and able them to perform data warehousing, Predictive and Machine learning solutions and visualizations. These all tools are market leader.This course provide deep understanding of Data extraction, Data analysis, Data interpretation, Data modeling ,Business Intelligence, Machine Learning ,deep learning and Data visualizations using the wide range of Tools, Technologies and Methodologies.
Our Business Intelligence and Data Warehousing Training offerings enlighten the students to understand BI technology initiatives with Data Science strategies and vision. This course does not require any Technical background and can be useful for those who want to switch to the data science field.

We Offer Comprehensive set of Skills from initial concept down to the Final Outcome.

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Machine learning with python
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Data visualizations with tableau and python
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ETL process with SQL and Hadoop

Course Content

Unit 1 : Python Programming


The Introduction to Python chapter will cover everything basic, starting from installing Python to writing basic syntax and code . The history of Python, introduction to Python workspace and working environment is briefly discussed in this introduction part.
The data types in python chapter will cover an overview of the various data types that are supported in Python interface. This chapter will cover the basic as well as advanced data types such as integer, float etc.
The data structures in python chapter will include the basic data structure such as list, tuple, dictionary etc. with which data can be given as input in Python.
The Pandas Module chapter will cover a brief introduction to work with Pandas and execute Python codes in Pandas. The chapter will also cover how to install the basic packages and use them in the pandas interface.
The data frames are an essential data structure that pandas support to code in python. The chapter will specifically demonstrate the various functionality and utility of pandas data frames with practical examples on it.
The working with text, time and date chapter includes the utility of various date functions and usage of packages that supports functionality for Date such as “datetime” package in python. Working with text includes usage of text functions and understanding their functionality with practical examples.
This chapter on python will include working with the data frame structure of the dataset and performing various functionalities on the datasets such as merging, joing and performing group by using pandas.
The data visualization with Python will cover the practical implementation and visuals of the plots and charts created through Seaborn and Matplotlib package and also it covers an overview of these packages in Python.
The Descriptive Statistics chapter will cover the main elements of descriptive statistics, i.e., working with mean, median, variance, quantiles etc. in R with their practical implementation in Jupyter Notebook .
This Chapter will cover an overview of the statistical tests and methods on the various datasets that are available in pandas. The statistical tests that will be covered under this section includes application of Z-test, T-test, utility of normal distributed data etc. Also topics like sampling, bootstrapping, linear regression and correlation are covered in this chapter.
The topics that are covered under the supervised learning chapter are regression, classification models such as Logistic Regression, Support Vector Machine, KNN etc. Also the practical implementation of the models are demonstrated using Python.
The unsupervised learning chapter will cover and provide an introduction to the clustering models like K-means and a brief introduction to the Association Rule Mining Algorithm in Jupyter Notebook.
The Recommender system includes working on a practical example that builds a rating wise recommender system. The chapter will cover the algorithm of the Recommender system and its model building with Python.
The Deep Learning chapter includes an overview and practical applications of the models such as ANN, CNN and RNN in the Jupyter Notebook using Python Syntax.
The chapter will cover a brief introduction and practical application and implementation of Python and connecting Python interface with other databases such as MS SQL.

Unit 2 : Big data


Under this topic we will learn and explore about Big Data and Hadoop with the importance of Hadoop. Also the challenges that you face in Big Data. The Fundamental design principles and the technologies used in Hadoop will also be covered here. You will learn about the architecture, RDBMS and use cases of Hadoop.
In this section you will learn about the Hadoop Cluster and the Architecture of Hadoop cluster. Here you will get to learn about the workflow in cluster. The way of reading files from HDFS and writing files to HDFS will be covered under this section.
In this section we will get the basic idea of Map Reduce and its necessity. Also you will get the idea about the terms mapper , reducer and shuffling. The concept of usage of map reduce and its flow will be covered under this section.
In this section you will learn about the terminology like Combiner, Partitioner and Counter. You will get the basic idea about the Input formats and Output formats. Also the concept of mapping and reducing join using MR. Also learn about the configuration of Hadoop.
In this section you will get an idea about the challenges or drawback of using Hadoop 1.0. Then you will learn about the new features added in Hadoop 2.0. Also its Cluster Architecture and Federation. The concept of Yarn, Hadoop Ecosystem and the Yarn MR Application flow will be covered in this section.
In this section you will learn about basic concept of Pig, its features, its use cases. Also you will learn how to interact with Pig. It will also cover the idea of Basic data analysis and the Latin syntax of Pig. Also you will how data is loaded , its data types ,field definitions and the output of data.
In this section you will learn about view of schema, how to filter and sorting data. The commonly used functions, processing complex data . It will cover the concept of grouping data and also techniques of combining data sets. Also you will get idea about how to join data sets and split data sets in Pig.
In this section you will explore about the fundamentals, architecture of Hive and how to load data and apply query on data in Hive. It will also cover the data types, operators and functions of HiveQL. You will also learn about hive tables, managed tables and external table. Also about Storage formats, Importing data , Altering tables and dropping table. It will also cover about data query, sorting and aggregating data and scripts of Map Reduce.
Under this section you will learn about CAP Theorem. Also you will learn about the concept and architecture of HBase. Also you will learn about Client API’s and its features. The section will also cover the data models and operations over it.
In this section you will learn about the basic concept of Sqoop. You will also explore about connecting relational database using Sqoop. You will also learn about importing and exporting data from and to MySQL respectively. Then also how to further connect MySQL data to hive and hbase. You will also learn about using queries in Sqoop.
In this section you will learn the basic concept of Flume, its importance, architecture and configuration. Then you will learn about the concept of Oozie, its architecture and its configuration. It will also cover about the properties of Oozie and Job Submission in Oozie.
In this section you will learn about you will learn about the basics of Apache Spark, its importance and benefits. It will also cover the overview of Batch Analytics in Hadoop Ecosystem and also real time analytics options. It will give an idea about how Spark is beneficial to professionals. Under this section you will also learn about Spark components and Executive architecture of Spark.
In this section you will learn about features of Scala. Also you will explore about Basic data types, Objects, Classes, List, and Maps in Scala. You will also learn about usage of function as object and usage of Anonymous Functions, along with higher order functions in Scala. You will also learn about Pattern matching, Traits and Collections in Scala.
In this section you will learn about Spark, its Application and its deployment. You will also get idea about Distributed Systems. You will also get to know about Spark for scalable system and its execution context. You will also get to learn about Parallelism and Caching in Spark. The section will also cover basic idea about RDD, its deep dive, dependencies and lineage.
In this section you will learn about Transformations, Actions, Clusters also about the Data Frames in Spark. Also you will learn about the basic introduction of SQL. You will also explore about Spark SQL with CSV, JSON and Database.
In this section you will learn about Features of Spark Streaming and its use case. Also you will learn about Dstreams and about Transformations on it. Also you will explore about Hadoop and its importance. In this you will learn about Distributed Computation and Functional Programming. Also you will get to know about MapReduce Framework and its job run.

Unit 3 : SQL


• Introduction to T-SQL
• What is SQL Server
• What is SSMS
• What is Database
• Datatypes in SQL
• DDL Commands (Create, Alter, Drop)
• DML Commands (Select, Insert, Update, Delete, Truncate)
• Clauses ( Order by, Where By, Group By, Having)
• Aggregate Function
• Distinct, Null Values
• Operators (Arithmetic, logical, Conditional)
 o Arithmetic (+,-,/,*,++,--)
 o Logical Operator (<,>,<=,>=,!=,==)
 o Conditional (and, or)
 o In, not in, is null, between

• Alias Name for column and table name
• Introduction to Views
• Inner Join
• Left Join
• Right Join
• Full Outer Join
• Cross Join
• Self Join
• Introduction to SP
• Create Parameterized SP
• Execution SP
• Introduction to Trigger
• Creating trigger
• Creating cursor
• Use of trigger and cursor
• Introduction to Functions
• Types of funtions
o User Define function
 Inline table value function  Multiline table value function  Scalar funtion
o Built in function
 Date Funtion  String Function  Row Number  Rank, Dense Rank  Colease, is null
• Case when
• Union All

Unit 4 : Tableau


Under this section we will cover the basics of Tableau. Also it will give the insights of the importance and beneficial measures about working with Tableau. With this you will be able to clarify the quest about learning Tableau.
Under this we will learn how to connect or import data from different data sources. Also you will learn how different format of data can be imported to tableau.
Under this section you will cover about how you can clean data with the help of tableau. In general data cleaning is not done in tableau in vast area but in this we can clean data in minor level. In this you will learn the usage and working of data interpreter tool.
Under this you will learn about the actual workspace where you we will be working on the data set. You will be learning about how the different features will be useful for showing the different insights of data which will make data in much interactive manner.
Under this you will learn how to make data much more concise. This will make the data much more optimized which will help in depicting data in much more meaningful way. Mostly the data is prepared by removing the unwanted and noisy values from the data.
In this section you will learn about how to show data through different appropriate graph according to the data. Different formatting methods can be applied in graph to make the data in graphs appropriately descriptive. Different formatting filters will help in visualizing data in a understanding way.
In this section you will learn about the different filters in detail. By applying these filters as and when required on the graph will make the graphs much more informative. Also you will learn about how to edit the names of the data present on the row. Also we can change the diameters by ranging the values rather than the single value.
Under this section you will learn about creating additional fields. This is done when you need to analyse something different that is not given directly in the data source. You will learn in detail by creating new field with the help of existing fields simply or by applying conditions on them.
Under this particular section you will explore more filter tools which will make data more exploratory.
In this topic you will get to learn about framing the dashboard, which is basically concatenation of different graphs. In dashboard you bring different sheets depicting relative information on one sheet. Also you can add filter to it so if you change anywhere on dashboard it will reflect accordingly and vice versa.
Under this topic you will explore about how you can share your tableau project to client. This is one of the vital step in tableau course. With the help of publishing function you can create a simple link which can be then shared with the remote users to make them access the project that you made.
In this section you will learn about how you can create a full explanatory storyline. This is basically the sequence of different graph. Addition to this along with every sheet you can write caption or a short story guide with every sheet which will give the guideline narrating a sort of story. This will help you in explaining the data more concisely and optimally.

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