Next Batch -
  • 21/12/2020
  • |
  • Tuesday
  • |
  • 03:30 AM
Register For Free Demo

Course Features

  • Complimentary Life time Access to Core Java Online course
  • Course mentored by Industry expert
  • Project-based learning which will add stars to your resume
  • Course completion certificate
  • 1 Minor Project based on real-world applications

Course Overview

  • This course will help you to learn Python Programming Language which is basic to build Scientific Computing, Data Science, Data Analytics, Machine Learing and AI models and general purpose web applications.. The course will be mentored & guided by an Industry expert having hands-on experience in the design, development & maintenance of Java based web applications.The course includes 2 minor projects based on real-world applications with guided lab sessions.
  • It will be an online live (Live Stream) class, so you can attend this class from any geographical location. It will be an interactive live session, where you can ask your doubts to the instructor (similar to an offline classroom program).
  • Pre-requisites: Will to learn
  • Recommended for: Anyone who wants to learn and build Data Analytics/ ML/ AI Model or Python-based web applications, specifically
    • 1. College students who are looking for training in Python/ NumPy/ Pandas/ Matplotlib/ Django/ Flask
    • 2. Working Professionals who want to learn backend development with Python / Django/ Flask or Data Analytics

Course Mentor

  • Nitesh Sir, synonymous for C, C++, Data Structure and Python Training is a very seasoned trainer loved by students from last 11 years. His knowledge and delivery style for any entry level programming languages training for beginners specifically ' C, C++, Data Structure & Python ' is appreciated not only by graduates, undergraduates but by professionals also who are working on these technologies. He is the most preferred trainer of beginners.

Course Content

  • Data, Database Concept
  • Entity, Relation
  • RDBMS Concept
  • Normalization
  • Codd’s Rules
  • List the capabilities of SQL SELECT statements
  • Execute a basic SELECT statement
  • Projection and selection of the rows
  • Limit the rows that are retrieved by a query
  • Sort the rows that are retrieved by a query
  • Use ampersand substitution to restrict and sort output at runtime
  • Describe various types of functions available in SQL
  • Use character, number, and date functions in SELECT statements
  • Describe various types of conversion functions that are available in SQL
  • Use the TO_CHAR, TO_NUMBER, and TO_DATE conversion functions
  • Apply conditional expressions in a SELECT statement (CASE, DECODE)
  • Identify the available group functions
  • Describe the use of group functions
  • Group data by using the GROUP BY clause
  • Include or exclude grouped rows by using the HAVING clause
  • Write SELECT statements to access data from more than one table using equijoins and no equijoins
  • Join a table to itself by using a self- join
  • View data that generally does not meet a join condition by using outer joins
  • Generate a Cartesian product of all rows from two or more tables
  • Define subqueries
  • Describe the types of problems that the subqueries can solve
  • List the types of subqueries
  • Write single-row and multiple- row subqueries
  • Describe set operators
  • Use a set operator to combine multiple queries into a single query
  • Control the order of rows returned
  • Describe each data manipulation language (DML) statement
  • Insert rows into a table
  • Update rows in a table
  • Delete rows from a table
  • Control transactions
  • Advance DML statement:- multiple Insert, Insert all
  • Categorize the main database objects
  • Review the table structure
  • List the data types that are available for columns
  • Create a simple table
  • Explain how constraints are created at the time of table creation
  • Flashback
  • Functionality of recycle Bin
  • Purge
  • Describe how schema objects work
  • Create simple and complex views
  • Retrieve data from views
  • Inline views
  • Top –N Analysis
  • Create, maintain, and use sequences
  • Create and maintain indexes
  • Create private and public synonyms
  • System security and data security
  • User creation and management
  • grant, revoke, with grant option
  • System Privileges
  • Objects Privileges
  • Public synonyms
  • Django Models
  • Introduction, USES, INSTALLATION
  • Ndarray object, Data Types, data type object-dtype
  • Various Array Attributes of NumPy - shape, ndim,itemsize, etc.
  • Array Creation Routines- numpy.empty, numpy,zeros, numpy.ones
  • Array from Existing Data- numpy.arange, numpy.linspace, numpy.logspace
  • Array from Numerical Ranges- numpy.arange, numpy.limspace,numpy.logspace
  • NumPy- Indexing & Slicing
  • NumPy- Advanced Indexing- Boolean
  • Array Indexing, Integer Indexing
  • NumPy- Broadcasting
  • NumPy- Iterating over Array- nditer, Iteration Order, Modifying Array Values, External Loop, Broadcasting Iteration
  • NumPy- Array Manipulation-Changing Shape, Transpose Operations, Changing Dimensions, Joining Arrays, Splitting Arrays, Adding/Removing Elements
  • NumPy- Binary Operators
  • NumPy- String Functions
  • NumPy- Mathematical functions
  • NumPy- Arithmetic Operations
  • NumPy- Statistical Functions
  • NumPy- Sort, Search & Counting Functions
  • NumPy- Copies & Views
  • NumPy- Matrix Library
  • NumPy- Linear Algebra
  • I/O with NumPy
  • Installation
  • Introduction to data structures in Pandas: Series, Data Frame and
  • Operations on a Series:head,tail,vector operations
  • Data Frame operations: create, display, iteration, select column, add column, delete column
  • Binary operations in a Data Frame: add, sub, mul, div, radd, rsub
  • Matching and broadcasting operations
  • Missing data and filling values.
  • Comparisons, Boolean reductions, comparing Series, and combining Data Frames
  • Transfer data between CSV files/SQL databases, and Data Frame objects
  • Advanced operations on Data Frames: pivoting, sorting, and aggregation,
  • GroupBy- Split Data into Groups, View Groups, Iterating through Groups, Select a Group.
  • Aggregations -Multiple Aggregation, Filtration; Categorical Data, Indexing and Selecting Data
  • Descriptive statistics: min, max, mode, mean, count, sum, median, quartile, var
  • Create a histogram, and quantiles.
  • Function Application: pipe, apply, aggregation, transform and apply map
  • Function Applicaton- Tablewise , Row or Column-wise Function Application, Element-wise Function Application
  • ReIndexing - Reindexing, Reindex to Align with Other Objects, Filling while Reindexing, Renaming.
  • iteration - By Label, By Actual Value
  • Working with Text Data - Data Functionality; Timedelta , and altering labels.
  • Options and Customisation- get_option(), set_option(), describe_option(), option_context();
  • Merging /Joining; concatenation IO Tools- read_csv, read_table, Comparison with SQL
  • Basic Plotting -Plot, Bar Pilot, Histograms, Box Pilots, Area Pilot, Scatter Plot, Pie Chart.
  • MATPlot Library
Next Batch -
  • 21/12/2020
  • |
  • Tuesday
  • |
  • 03:30 AM
Register For Free Demo

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Advance Python- SQL - for Data Handling
Machine Learning -Numpy Library for statistical operations,
DataScience -Panda Framework for data analytics
Visualization-matplotlib,aplotting library

Duration : 35 hours

SSI's SQL for Data Analytics online course teaches students/developers the most in demand data retieving language SQL.Through hands-on learning you’ll load, extract, and manipulate data from relational databases. Study at your own pace and grow your SQL skills.

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