Data Analytics combo typically refers to the strategic combination of various data analytics tools, techniques, and technologies to derive valuable insights and make informed decisions from data. Data analytics involves the systematic analysis of data to uncover patterns, trends, and actionable insights.A Data Analytics combo involves using the appropriate combination of these methods and tools to extract valuable insights from data, whether it’s for business intelligence, marketing analysis, scientific research, or any other domain.
Job Oriented Course for Data Analytics
Technologies Included in Data Analytics Course:-
Duration of Training : 40 hrs
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Data Analytics Training & Certification in Pune
Highly Experienced Certified Trainer with 10+ yrs Exp. in Industry
Realtime Projects, Scenarios & Assignments
COURSE CONTENT :
ORACLE 12C SQL
WEEK – 1 :
1] Introduction to Oracle DB 12c
Oracle Database 12c: Focus Areas, Fusion Middle-ware, Oracle Cloud, Services, Deployment Methods, RDBMS, Data Model, ER Diagram, Relation DB Terminology, SQL Statements and Development Environment, HR Schema Tables, DB Documentation.
2] Retrieving Data using SQL SELECT statement
Basic Select Statement, Arithmetic Expression, Defining Null values, Concatenation Operator & Literal, Duplicate Rows, Displaying table structure. Practice Overview.
3] Restricting & Sorting Data
Use of Where clause, Character Strings & Date Data, Comparison, Logical, Range, Pattern, & Other operators, Rules Precedence, Order By Clause, Substitution variables. Practice Overview
WEEK – 2 :
4] Single Row Function & Customized Output
SQL function, Single Row Function, Character/Case Functions, Number Functions, Nesting Function, Date Functions etc. Practice Overview.
5] Conversion & Conditional Expressions
Conversion Function, Implicit/Explicit Data Conversion, General Function with covering NVL, NVL2, NULLIF, COALESCE, Conditional Expression with Decode function and Case Expression. Practice Overview.
6] Reporting Aggregated Data using Group Function
Usages of Group function, Nesting Group Function, Creating Group Data, Restricting group data using Having Clause. Practice Overview.
7] Displaying Data from Multiple Tables using Join
Type of Joins, Explaining with Natural Join, Using Clause, On Clause, SQL 99 Syntax, Three-Way Join, Self Join, NonEqui Join, Inner Versus Outer Join, Cross Join etc. Practice overview.
WEEK – 3 :
8] Using Sub-queries to solve queries
Scenario to use Sub-query, Rules for Sub-queries, Type of Sub-queries, Single Row Sub-queries and Multi Row Sub-queries. Null value in Sub-query etc. Practice Overview.
SQL Advanced :
9] Using Set Operator
Set Operator Rules, Covering Set Operator as UNION, UNION ALL, INTERSECT, MINUS etc. Practice Overview.
10] Managing Tables using DML Statements
Data Manipulation Language, Covering INSERT, UPDATE, DELETE & DB Transaction control using COMMIT, ROLLBACK, SAVEPOINT. Use of For update clause in SELECT Statement.
11] Introduction to Data Definition Language
Database Object, Naming Rules, Various Data Types, CREATE TABLE statement, Constraint guidelines, Defining constraints as NOT NULL, UNIQUE, PRIMARY KEY, FOREIGN KEY & CHECK etc. ALTER TABLE, DROP TABLE Statement. Create Table using Sub-query. Practice Overview.
WEEK – 4 :
12] Introduction to Data Dictionary Views
Data Dictionary Structure, How to use Data Dictionary, USER_OBJECTS, ALL_OBJECT, USER_CONSTRAININTS. TABLE Information, Column Information etc. Practice overview.
13] Creating Sequence, Synonyms & Indexes
Importance of Sequence, Synonyms & Indexes. Defining Sequence, Synonyms & Indexes & DROP statement etc. Practice overview.
14] Creating Views
What’s View? Advantage of Views, CRETAE Simple & Complex views, Rules for performing DML operation on views. Modifying & Removing a view etc. Practice Overview.
15] Retrieving Data by using Sub-Query
Multiple Column Sub-query, Column Comparison, Pair & Non Pair Sub-queries, Scalar Sub queries, Co-related Sub-queries, Use of WITH clause. Practice Overview.
Introduction to Python programming
Variables, data types, operators, and expressions
Control structures (if/else, loops)
Functions, modules, and packages
Data structures in Python
Lists, tuples, dictionaries, and sets
Indexing, slicing, and manipulating data structures
Python for data analysis
Reading and writing data from various formats (CSV, Excel, JSON, SQL)
Exploratory data analysis with Pandas library
Cleaning, transforming, and aggregating data
Predictive modeling and machine learning algorithms
Visualizing data with Matplotlib and Seaborn
evaluation and selection
Working with time series data
Case studies and projects
Analyzing real-world datasets and solving data-related problems
Developing data-driven insights and recommendations.
Data Transformations :
Data Modelling :
Visualizing your Data :
Working with PBI Service :
Working with Excel :
Organization Packs, Security and Groups :
Data Analytics Combo Online Training from India | Excel | Autofill | Demo - Radical Technologies