Best Data Science training for employees| KVCH Nigeria

Data Science Course Overview

Data science is one of the most popular professions of the decade, and there has never been a greater need for people who can analyse data and explain the results so that decisions can be made based on the data. This Professional training by KVCH will help anyone who wants to work in data science learn skills that are useful for data scientists. KVCH is a leading provider of corporate training for data science in Lagos.

It's not true that you need a Ph.D. to become a data scientist. This Professional Certificate can be taken by anyone who wants to learn, even if they don't know anything about computer science or programming languages. If you’re looking for in-house training for employees in data science, KVCH is your go-to- option. It will offer them skill upgradation, tools, and the portfolio they need to stand out as a data scientist.

Enrol in our course to learn data science training in Ikeja. The curriculum of the programme will teach you the most up-to-date tools and skills that will help you get a job, such as open source tools and libraries, Python, databases, SQL, data visualisation, data analysis, statistical analysis, predictive modelling, and machine learning algorithms. You will learn data science by using real data science tools and data sets from the real world.

The success of KVCH's Data Science training programme can be measured by the amount of specialised instruction and focused study that is given, with an emphasis on developing individual skills rather than giving out general information. Join KVCH's Data Science training programme to enhance the efficiency of employees. We are a trusted provider of data science training for employees.

The Data Science Training certification is the most in-depth course we offer. It is designed to help professionals learn the skills and knowledge they need as a data scientist. As a professional, you can learn all about data science by working on use cases and projects that are up-to-date and then putting what you've learned to use in the real world.

When you finish this data science course, KVCH will give you a certificate that proves you know how to use cutting-edge data science methods and makes you a data science expert in your organisation.

When you enrol in our Data Science Training Program, you can learn the following:

Show that you know how to use statistical methods to look at data in a business setting.
Use techniques from Data Science to help your company solve problems.
Tools for data mining can be useful in the real world.
Use the most up-to-date tools and techniques to analyse Big Data.
Create smart machines by using the logic of computing.
Use ideas about teamwork, leadership, making decisions, and organisational theory in real-world situations.

Data science remains a promising and in-demand field for skilled workers. A job in data science can be interesting and pay well, but it's not easy to get started in this field. To become a data science professional, you do not need a bachelor's degree or a master's degree. One needs the right set of skills and experience which you can get by enrolling in our course.

The following are some potential professions for you to consider:

Data Engineer
Business Intelligence Analyst
Marketing Analyst
Data Architect
Machine Learning Engineer
Database Administrator

In recent years, the median salary for people who work in data science has gone up a lot. This growth is mostly due to the fact that more and more industries need data science experts to unlock the potential of the data they already have. As a result, the rise in demand for qualified data scientists was a big reason why the median salary went up overall.

The median salary of a data scientist in the USA is 146,000 USD per year.
In the US, a data scientist's salary can go up by about 12% every 17 months.
A data scientist who is just starting out can make up to $94,600 per year.
The average salary for a data scientist with two to five years of experience is $116,000. This is 23% more than the starting salary for a data scientist.

Companies can use Data Science to rapidly and reliably analyse vast volumes of data from many sources to get useful insights for making better decisions. Data science has numerous potential applications, including but not limited to those in marketing, healthcare, banking, finance, government, and many more.

When you've gotten good at data science and have a lot of experience, you can move into related fields like marketing and sales, data quality, finance, and business intelligence, or even work as a consultant for a data-heavy industry leader.

You will learn how to use data analytics to manage and evaluate large and complicated datasets, as well as to find problems and offer solutions.
Data analytics, which is the process of collecting and organising data for analysis and prediction, is important for making important business decisions and putting in place needed changes.
A person who takes this Data Science Training course will learn how to extract data in different ways that could help their employer succeed.

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Data Science Curriculum

Data Science is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents.

Topics:

Python Statistics for Data Science
Databases – MySQL and SQL Queries
Data Science Master’s Program
Machine Learning
Deep Learning
Power BI
Hadoop- Apache Spark and Scala Certification
Tableau - For Data Visualization
Data Science Masters - Live Projects

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Introduction To Python

Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.

Introduction To Variables

Variables, expression condition and function
Global and Local Variables in Python
Packing and Unpacking Arguments
Type Casting in Python
Byte objects vs. string in Python
Variable Scope

Python Data Type

Declaring and usingNumeric data types
Using stringdata type and string operations
Understanding Non-numeric data types
Understanding the concept of Casting and Boolean.
Strings
List
Tuples
Dictionary
Sets

Introduction Keywords and Identifiers and Operators

Python Keyword and Identifiers
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators

List

What is List.
List Creation
List Length
List Append
List Insert
List Remove
List Append & Extend using “+” and Keyword
List Delete
List related Keyword in Python
List Revers
List Sorting
List having Multiple Reference
String Split to create a List
List Indexing
List Slicing
List count and Looping
List Comprehension and Nested Comprehension

Dictionary

Dict Creation
Dict Access (Accessing Dict Values)
Dict Get Method
Dict Add or Modify Elements
Dict Copy
Dict From Keys.
Dict Items
Dict Keys (Updating, Removing and Iterating)
Dict Values
Dict Comprehension
Default Dictionaries
Ordered Dictionaries
Looping Dictionaries
Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets, Tuples and Looping Programming

Sets

What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
Frozen Sets

Tuple

What is Tuple
Tuple Creation
Accessing Elements in Tuple
Changinga Tuple
TupleDeletion
Tuple Count
Tuple Index
TupleMembership
TupleBuilt in Function (Length, Sort)

Control Flow

Loops
Loops and Control Statements (Continue, Break and Pass).
Looping techniques in Python
How to use Range function in Loop
Programs for printing Patterns in Python
How to use if and else with Loop
Use of Switch Function in Loop
Elegant way of Python Iteration
Generator in Python
How to use nested IF and Else in Python
How to use nested Loop in Python
Use If and Else in for and While Loop
Examples of Looping with Break and Continue Statements
How to use IN or NOTkeywordin Python Loop.

Exception and File Handling, Module, Function and Packages

Python Exception Handling

Python Errors and Built-in-Exceptions
Exception handing Try, Except and Finally
Catching Exceptions in Python
Catching Specific Exception in Python
Raising Exception
Try and Finally

Python File Handling

Opening a File
Python File Modes
Closing File
Writing to a File
Reading from a File
Renaming and Deleting Files in Python
Python Directory and File Management
List Directories and Files
Making New Directory
Changing Directory

Python Function, Modules and Packages

Python Syntax
Function Call
Return Statement
Write an Empty Function in Python –pass statement.
Lamda/ Anonymous Function
*argsand **kwargs
Help function in Python
Scope and Life Time of Variable in Python Function
Nested Loop in Python Function
Recursive Function and Its Advantage and Disadvantage
Organizing python codes using functions
Organizing python projects into modules
Importing own module as well as external modules
Understanding Packages
Programming using functions, modules & external packages
Map, Filter and Reduce function with Lambda Function
More example of Python Function

Data Automation (Excel, SQL, PDF etc)

Python Object Oriented Programming—Oops

Concept of Class, Object and Instances
Constructor, Class attributes and Destructors
Real time use of class in live projects
Inheritance, Overlapping and Overloading operators
Adding and retrieving dynamic attributes of classes
Programming using Oops support

Python Database Interaction

SQL Database connection using
Creating and searching tables
Reading and Storing configinformation on database
Programming using database connections

Reading an excel

Reading an excel file usingPython
Writing toan excel sheet using Python
Python| Reading an excel file
Python | Writing an excel file
Adjusting Rows and Column using Python
ArithmeticOperation in Excel file.
Plotting Pie Charts
Plotting Area Charts
Plotting Bar or Column Charts using Python.
Plotting Doughnut Chartslusing Python.
Consolidationof Excel File using Python
Split of Excel File Using Python.
Play with Workbook, Sheets and Cells in Excel using Python
Creating and Removing Sheets
Formatting the Excel File Data
More example of Python Function

Working with PDF and MS Word using Python

Extracting Text from PDFs
Creating PDFs
Copy Pages
Split PDF
Combining pages from many PDFs
Rotating PDF’s Pages

Complete Understanding of OS Module of Python

Check Dirs. (exist or not)
How to split path and extension
How to get user profile detail
Get the path of Desktop, Documents, Downloads etc.
Handle the File System Organization using OS
How to get any files and folder’s details using OS

Data Analysis & Visualization

Pandas

Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to files
How to get record specific records Using Pandas Adding & Resetting Columns, Mapping with function
Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
Exploring the Data Plotting, Correlations, and Histograms
Getting statistical information about the data Analysis Concepts, Handle the None Values
Reading files with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
Applying formulas on the columns Basic Grouping, Concepts of Aggregate Function
Complete Understanding of Pivot Table Data Slicing using iLocand Locproperty (Setting Indices)
Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
Exporting the results to Excel Joins:
Python | Pandas Data Frame Inner Join
Under sting the properties of Data Frame Left Join (Left Outer Join)
Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
Pandas | Find Missing Data and Fill and Drop NA Appending DataFrameand Data
Pandas | How to Group Data How to apply Lambda / Function on Data Frame
Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

NumPy

Introduction to NumPy: Numerical Python
Importing NumPy and Its Properties
NumPy Arrays
Creating an Array from a CSV
Operations an Array from aCSV
Operations with NumPy Arrays
Two-Dimensional Array
Selecting Elements from 1-D Array
Selecting Elements from 2-D Array
Logical Operation with Arrays
Indexing NumPy elements using conditionals
NumPy’sMean and Axis
NumPy’sMode, Median and Sum Function
NumPy’sSort Function and More

MatPlotLib

Bar Chart using Python MatPlotLib
Column Chart using Python MatPlotLib
Pie Chart using Python MatPlotLib
Area Chart using Python MatPlotLib
Scatter Plot Chart using Python MatPlotLib
Play with Charts Properties Using MatPlotLib
Export the Chart as Image
Understanding plt. subplots () notation
Legend Alignment of Chart using MatPlotLib
Create Charts as Image
Other Useful Properties of Charts.
Complete Understanding of Histograms
Plotting Different Charts, Labels, and Labels Alignment etc.

Introduction to Seaborn

Introduction to Seaborn
Making a scatter plot with lists
Making a count plot with a list
Using Pandas with seaborn
Tidy vs Untidy data
Making a count plot with a Dataframe
Adding a third variable with hue
Hue and scattera plots
Hue and count plots

Visualizing Two Quantitative Variables

Introduction to relational plots and subplots
Creating subplots with col and row
Customizing scatters plots
Changing the size of scatter plot points
Changing the style of scatter plot points
Introduction to line plots
Interpreting line plots
Visualizing standard deviation with line plots
Plotting subgroups in line plots

Visualizing a Categorical and a Quantitative Variable

Current plots and bar plots
Count plots
Bar plot with percentages
Customizing bar plots
Box plots
Create and interpret a box plot
Omitting outliers
Adjusting the whiskers
Point plots
Customizing points plots
Point plot with subgroups

Customizing Seaborn Plots

Changing plot style and colour
Changing style and palette
Changing the scale
Using a custom palette
Adding titles and labels: Part 1
Face Grids vs. Axes Subplots
Adding a title to a face Grid object
Adding title and labels: Part 2
Adding a title and axis labels
Rotating x-tics labels
Putting it all together
Box plot with subgroups
Bar plot with subgroups and subplots

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Python - MySQL

Introduction to MySQL
What is the MySQLdb
How do I Install MySQLdb
Connecting to the MYSQL
Selecting a database
Adding data to a table
Executing multiple queries
Exporting and Importing data tables.

SQL Functions

Single Row Functions
Character Functions, Number Function, Round, Truncate, Mod, Max, Min, Date

General Functions

Count, Average, Sum, Now etc.

Joining Tables

Obtaining data from Multiple Tables
Types of Joins (Inner Join, Left Join, Right Join & Full Join)
Sub-Queries Vs. Joins

Operators (Data using Group Function)

Distinct, Order by, Group by, Equal to etc.

Database Objects (Constraints & Views)

Not Null
Unique
Primary Key
Foreign Key

SQL Basic

SQL Introduction
SQL Syntax
SQL Select
SQL Distinct
SQL Where
SQL And & Or
SQL Order By
SQL Insert
SQL Update
SQL Delete

SQL Advance

SQL Like
SQL Wildcards
SQL In
SQL Between
SQL Alias
SQL Joins
SQL Inner Join
SQL Left Join
SQL Right Join
SQL Full Join
SQL Union

SQL Functions

SQL Avg()
SQL Count()
SQL First()
SQL Last()
SQL Max()
SQL Min()
SQL Sum()
SQL Group By

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Introduction to Data Science

What is Analytics & Data Science
Common Terms in Analytics
What is data
Classification of data
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem-solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project
Why Python for data science

Accessing/Importing and Exporting Data

Importing Data from various sources (Csv, txt, excel, access etc)
Database Input (Connecting to database)
Viewing Data objects - sub setting, methods
Exporting Data to various formats
Important python modules: Pandas

Data Manipulation: Cleansing - Munging Using Python Modules

Cleansing Data with Python
Filling missing values using lambda function and concept of Skewness.
Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
Normalizing data
Feature Engineering
Feature Selection
Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
Label encoding/one hot encoding

Data Analysis: Visualization Using Python

Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)
Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)

Introduction to Statistics

Descriptive Statistics
Sample vs Population Statistics
Random variables
Probability distribution functions
Expected value
Normal distribution
Gaussian distribution
Z-score
Central limit theorem
Spread and Dispersion
Inferential Statistics-Sampling
Hypothesis testing
Z-stats vs T-stats
Type 1 & Type 2 error
Confidence Interval
ANOVA Test
Chi Square Test
T-test 1-Tail 2-Tail Test
Correlation and Co-variance

Introduction to Predictive Modelling

Concept of model in analytics and how it is used
Common terminology used in Analytics & Modelling process
Popular Modelling algorithms
Types of Business problems - Mapping of Techniques
Different Phases of Predictive Modelling

Data Exploration for Modelling

Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers’ data
Imbalanced Data Techniques

Data Pre-Processing & Data Mining