Data Science Training Course in Los Angeles, California

Data Science Training

Course Amount: $999

S No	Start Date	Action
1	Sep-15-2021

Data Science Training in Los Angeles, CA

It is easy to accelerate your career in the ever-growing field of Data Scientist with our exclusive, affordable Data Scientist online course in los angeles.

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills.

In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

In this Data Science course training, you will learn Data Analytics, statistical modelling, Machine Learning algorithms, recommendation engines, text mining, and more. You will also gain knowledge about statistical R calculations, developing e-commerce recommendation engines, and using market basket analytics in retail.

We are providing Data Science training with highly experienced real-time industry experts. Our team of Data Science trainers offers a deep in-depth trailing with real-time project scenarios with updated technologies by using simple programming tools.

KEY Highlights

Get noticed by top companies through our professional job assistance.

35 hours of in-depth training programs with realtime scenarions.

Get trained by the highly experienced certified industry experts.

Upgraded sylabus with Industry oriented concepts for in-depth knowledge.

15+ In- demand tools & skills will help in Technical Assistance.

Every session will be live and provides you hands-on experience training.

Training Advantages



35 contact hours



Industry case studies



Hands-on Projects



Real time training

Course Outcomes



Core Concepts

Core computer science concepts from leading industry experts.



Application Testing

Build an end-to-end application and test it with exciting features.



Certification

Earn an industry-recognized course completion certificate.

Data Science Certification Course Contents

Python Core (Complete Hands-on)

What is Python?
Why Python?
Installation of python
Conditions
Loops
Break statement
Continue statement
Range functions
Command-line arguments

Strings & Collections

String Object Basics

String Methods

Splitting and Joining Strings

String format functions

List Object Basics

List Methods

Tuples

Sets

Frozen sets

Iterators

Generators

Decorators

Python Advanced concepts

Creating Classes and Objects

Inheritance

Multiple Inheritance

Working with files

Reading and Writing Files

Using Standard Modules

Creating custom modules

Exceptions Handling with Try-except

Finally, in exception handling

NUMPY

ND array Object

Data Types

Array Attributes

Array Creation Routines

Array from Existing Data

Array from Numerical Ranges

Indexing & Slicing

Advanced Indexing

Broadcasting

Iterating Over Array

Array Manipulation

Binary Operators

String Functions

Mathematical Functions

Arithmetic Operations

Statistical Functions

Sort, Search & Counting Functions

Byte Swapping

Copies & Views

Matrix Library

Pandas

Series

Data Frame

Panel

Basic Functionality

Re indexing

Iteration

Sorting

Working with Text Data

Options & Customization

Indexing & Selecting Data

Window Functions

Date Functionality

Time delta

Categorical Data

Visualization

IO Tools

Matplotlib

Introduction & Installation

Format strings in the plot function

Axes labels

Legend

Grid

Bar chart

Histograms

Pie chart

Save fig

Scatter plots

Sub plots

Seaborn

Introduction & Installation

Bar plot

Distributed plot

Box plot

Strip plot

Pair grid

Violin Plot

Cluster Map

Heat Map

Facet Grid

KDE plot

Joint plot

Reg plot

Pair plot

Features Engineering

Numerical variables

Categorical variables

Missing Values

Outliers

Mean and median imputation

Random sample imputation

Dummy variables

One hot encoding

Train and test data split

Save model using pickle

Machine Learning-Introduction

What is Machine Learning

Machine Learning Types supervised learning o Unsupervised learning o Reinforcement learning o Deep learning

Linear regression

Multiple linear regression

Gradient Descent

Ridge regression

Lasso regression

Logistic regression-Binary classification

Logistic regression-Multi Class classification

K Nearest Neighbors (KNN)

Naive Bayes

Decision trees

Random forests

Un Supervised Learning

K Means Clustering

Optimization techniques

K fold cross-validation

Hyperparameter tuning o Grid Search CV o Randomized CV

Ensemble Methods o Boosting o Bagging

Deep learning

Introduction to Tensor flow

Constant

Place holders

Variables

MLNN

Neurons

Weights

Activations

Networks of Neurons

Training Networks

Backpropagation

Gradient Descent

CNN

Classification learning

Flatten

Fully Connected  SoftMax

Interview Preparation

3 Real-Time Projects

Deployment on multiple platforms

Discussion on project explanation in the interview

Data scientist roles and responsibilities

Data scientist day to day work

One to One resume Discussion with project, technology and Experience.

Data Science Classes Los Angeles FAQ’S

What is the smallest unit of information commonly in use in today’s computers?

A Bit

What is the cost of each software license for the R open-source data analysis program?

R is free

What is the name of the data object that R uses to store a rectangular dataset of cases and variables?

A Data Frame

What do the following R functions stand for?

c () stands for concatenates data elements together
<- stands for assignment arrow
data frame () makes a data frame from separate vectors
str () reports the structure of a data object

summary () reports data modes/types and data overview

What are the most important skills for an aspiring data scientist to acquire?

Machine learning, data mining, information retrieval, statistics, data and information visualization, databases (modelling, organizing and querying), data structures including indexing schemes, programming including Python, R, SAS, Hadoop and Spark, graph/network analysis, natural language processing, optimization, and modelling & simulation.

What do the following R functions stand for?

Positively (right) skewed

What are the most common Big Data problems?

Modelling true risk
Customer churn analysis
Recommendation engine
Ad targeting
POS transaction analysis
Analyzing network data to predict failure
Threat analysis
Trade surveillance
Search Quality
Data sandbox

Which are the two main components of core Hadoop?

1.Hadoop Distributed File System (HDFS) 2.MapReduce

Name the tools typically used in Big Data scenarios?

1.NoSQL CouchDB, Cassandra, BigTable, Hbase

2.MapReduce Hadoop Hive Pig Cascading

3.Storage S3 Hadoop Distributed File System

4.Servers EC2, Beanstalk, Elastic, Herokee

5.Processing R, Mechanical Turk, Datameer, BigSheets, ElasticSearch

Request for more info

Comment

Customer Testimonials

Nothing makes us happier than satisfied clients. Let us share some successful client stories with you.



This is an excellent practical course, giving the opportunity to get acquainted with both theoretical and practical work in Business Analysis.

Henry James

Chicago



The best training professionals I found for automation testing training, faculties are highly experienced, explained every concept in-depth with real-time project scenarios.

Jennifer

San Diego



Happy to start off my selenium career here, this is by the far the best institute I have ever got for Selenium. Very eloquent tutorial. Outstanding Training with great examples.

Sophia

Dallas