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Data Science Training in Noida

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Data Science Training Course in Noida

Data Science Certification

Certification will be delivered as per Industry criterion for Data Science training which would benefit fresher/Experienced to up-skill at Corporate.

Experienced Faculty

Professional & expert Sr. Data Science / Data Science Analyst Trainers With 10+ Years of working experience will deliver the workshop sessions.

Placement Assistance

When 70% of Data Science training will be completed of learners then devoted placement team will start to process of scheduling your interviews.

Lab Facility

Expand your proficiency with extreme aligned of Corporate with set of technical test series with Data Science practical session to glaze your potential.

Data Science Workshop Sessions

Learners will grasp real time & Data Science practical training sessions with thorough case studies & workshops at SLA Noida

Admin Support

Will get 7 Days assistance for upcoming batch schedule/re-scheduling classes/arrange or ask to arrange a backup or dout sessions

In simple terms, data science is the study of data. It is primarily evolving approaches of registering, loading, and evaluating facts and figures to efficiently excerpt beneficial evidence. To gain insights and knowledge from any type of data — both structured and unstructured is the goal of data science. It is a blend of various tools, algorithms, and machine learning principles with the objective to discover hidden patterns from the raw data. SLA Consultants Noida offers Data Science Training Course from experienced instructors who not only give conceptual understanding but also provide hands-on training through live projects with real-world case studies of clients. This help learners to get all-inclusive learning of the Data Science Applications. Generally, the professionals who perform these activities are said to be a Data Scientist / Science professional. It is the most high-in-demand profession and as per Harvard and the most sort after a profession in the world. It can be said that data Science is a more forward-looking approach, an exploratory way with the focus on analyzing the past or current data and predicting future outcomes with the aim of making informed decisions. It gives answers to the open-ended questions as to “what” and “how” events occur.

SLA Consultants Noida helps participants to become professional Data Analyst that explains what is going on by processing history of the data. Data Science applications do the investigative evaluation to determine understandings from it, and likewise employs several cutting-edge algorithms (machine learning) to recognize the incidence of a specific happening in the upcoming time. SLA Consultants Noida covers all aspects of Data Science Applications including Data Science Training Course Python Data Visualization, Machine Learning by Using R-Programming, Python Machine & Deep Learning and Tableau/MS Power BI. SLA enables learners to understand Data Science in such a way that they know how to utilize Data Science tools to make choices and forecasts making usage of machine learning, predictive plus decision science (perspective analytics), and predictive causal analytics.

SLA Consultants Noida designed Data Science Training Course from Data Analyst or Business Analysts who give all-inclusive detailed learning in Data Analytics, Data Acquisition, Data Science, Project Life Cycle, Statistical Methods, And Machine Learning. You can gain expertise to deploy Recommenders using R programming, and you will also learn data analysis, data transformation, experimentation, and evaluation. Data Science Training Course by SLA Consultants Noida gives the most comprehensive Data Science course in the market, covering the complete Data Science lifecycle concepts from Data Integration, Data Collection, Exploration of Data, Data Mining, Data Cleansing, Feature Engineering, Data Transformation, Data Visualization, Structure Models (Prediction) and arranging the answer to the client.

This is a severe problem in a data-driven world that we are living in today. Many organizations are ready to pay top-dollar salaries for professionals with the right Data Science skills. This Data Science Training Course will provide you with all skills needed to master Data Science along with Big Data, Data Analytics, and R programming. All this means that you can fast-track your career to take on more lucrative and promising job roles and take your career to the next level.

The a bunding skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, XLMiner, Spark, Hadoop, Minitab, Tableau, programming languages like R programming, Python are covered extensively as part of this Data Science training. SLA Consulatnts Noida gives the opportunity to freshers, professionals, or job aspirants to excel in the field of Data Science to garb lucrative job prospect as a Data Analyst/Business Analyst in leading organizations.

Universally, the data that we had was mostly structured and small in size, which could be analyzed by using simple BI tools. The data in the traditional systems were mostly structured, today most of the data is unstructured or semi-structured.

The business organizations realized the value of analyzing the historical data in order to make informed decisions and improve their business. In all the walks of the business, digitization is helping them to generate the data and enabling the analysis of the data. This, in turn, is helping to create myriad data science/analytics job opportunities in this space.

This information is obtained from different sources like financial logs, text files, multimedia forms, sensors, and instruments. The simple BI tools are not capable of processing this huge volume and variety of data. This is the reason why we need more complex and advanced analytical tools and algorithms for processing, analyzing and drawing meaningful insights out of it.

As a result of digitalization process across the domains, it is creating tons of data and the demand for the Data Science professionals who can evaluate and extract meaningful insights is increasing and providing numerous of jobs in the space of Data Science.

Huge data will provide opportunities to drive key business decisions. Undoubtedly, It is soon going to change the way we look at the world deluged with data around us. Hence, a Data Scientist should be highly skilled and motivated to solve the most complex problems. Certainly, there is a huge void between the demand and supply and thereby creating ample job opportunities and salaries.

Data Scientists are considered to be the highest in the job market. The career path of data Scientists is long-lasting and rewarding as the data generation is increasing by leaps and bounds and the need for the Data Science professionals will increase perpetually.

Data Science Training Programme Modules

Data Science Training Course

Module 1 – Python Overview

Module 2 – Machine Learning by Using R-Programming

FUNDAMENTAL OF STATISTICS

Population and sample
Descriptive and Inferential Statistics
Statistical data analysis
Variables
Sample and Population Distributions
Interquartile range
Central Tendency
Normal Distribution
Skewness.
Boxplot
Five Number Summary
Standard deviation
Standard Error
Emperical Formula
Central limit theorem
Estimation
Confidence interval
Hypothesis testing
P-value
Scatterplot and correlation coefficient
Standard Error
Scales of Measurements and Data Types
Data Summarization
Visual Summarization
Numerical Summarization
Outliers & Summary

Module 2.1- Introduction to Data Analytics

Objectives:

This module introduces you to some of the important keywords in R like Business

Intelligence, Business
Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems.
This module tells you what is R and how it is used by the giants like Google, Facebook, etc.
Also, you will learn use of ‘R’ in the industry, this module also helps you compare R with other Software in analytics, install R and its packages.

Topics:

Business Analytics, Data, Information
Understanding Business Analytics and R
Compare R with other software in analytics
Install R

Module 2.2- Introduction to R programming

Starting and quitting R

Recording your work

Basic features of R.
Calculating with R
Named storage
Functions
R is case-sensitive
Listing the objects in the workspace
Vectors
Extracting elements from vectors
Vector arithmetic
Simple patterned vectors
Missing values and other special values
Character vectors Factors
More on extracting elements from vectors
Matrices and arrays
Data frames
Dates and times

NOTE:-

Assignments with Datasets

Import and Export data in R

Importing data in to R
CSV File
Excel File
Import data from text table

Topics

Variables in R
Scalars
Vectors
R Matrices
List
R – Data Frames
Using c, Cbind, Rbind, attach and detach functions in R
R – Factors
R – CSV Files
R – Excel File

NOTE-:

Assignments
Business Scenerio/Group Discussion.

R Nuts and Bolts-:

Entering Input. – Evaluation- R Objects- Numbers- Attributes- Creating Vectors- Mixing Objects-Explicit Coercion- Summary- Names- Data Frames.

Module 2.3- Managing Data Frames with the dplyr package

The dplyr Package
Installing the dplyr package
select()
filter()
arrange()
rename()
mutate()
group_by()
%>%

NOTE-:

Assignments
Business Scenerio/Group Discussion.

Module 2.5 – Data Manipulation in R Objectives:

In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting in a data set, which is ready for any analysis.
Thus using and exploring the popular functions required to clean data in R.

Topics

Data sorting
Find and remove duplicates record
Cleaning data
Merging data

Statistical Plotting-:

Bar charts and dot charts
Pie charts
Histograms
Box plots
Scatterplots
QQ plots

NOTE:-

Assignments with Datasets

Objectives:

Control Structure Programming with R
The for() loop
The if() statement
The while() loop
The repeat loop, and the break and next statements
Apply
Sapply
Lapply

NOTE:

Assignments with Datasets

Factors

Using Factors
Manipulating Factors
Numeric Factors
Creating Factors from Continuous Variables
Convert the variables in factors or in others.

Reshaping

Data Modifying
Data Frame Variables
Recoding Variables
The recode Function
Reshaping Data Frames
The reshape Package

NOTE:-

Assignments with Datasets

Explore many algorithms and models:

Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine!

Module 2.6- Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning

Machine Learning Languages, Types, and Examples
Machine Learning vs Statistical Modelling
Supervised vs Unsupervised Learning
Supervised Learning Classification
Unsupervised Learning

Module 2.7 – Supervised Learning I

K-Nearest Neighbors
Decision Trees
Random Forests
Reliability of Random Forests
Advantages & Disadvantages of Decision Trees

Module 2.8 – Supervised Learning II

Regression Algorithms
Model Evaluation
Model Evaluation: Overfitting&Underfitting
Understanding Different Evaluation Models

Module 2.9 – Unsupervised Learning

K-Means Clustering plus Advantages & Disadvantages
Hierarchical Clustering plus Advantages & Disadvantages
Measuring the Distances Between Clusters – Single Linkage Clustering
Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
Density-Based Clustering

Module 2.10 – Dimensionality Reduction & Collaborative Filtering

Dimensionality Reduction: Feature Extraction & Selection
Collaborative Filtering & Its Challenges

Module 2.11 – Classification

An Overview of Classification.
Why Not Linear Regression?
Logistic Regression
The Logistic Model
Estimating the Regression Coefficients
Making Predictions
Logistic Regression for 2 Response Classes
Lab: Logistic Regression.
The Stock Market Data
Logistic Regression

NOTE

Assignments with Different Datasets
Business Scenerio/Group Discussion

Module 2.12 – Tree-Based Methods

The Basics of Decision Trees
Regression Trees
Classification Trees
Trees Versus Linear Models
Advantages and Disadvantages of Trees
Bagging, Random Forests, Boosting
Bagging
Random Forests
Lab: Decision Trees
Fitting Classification Trees
Fitting Regression Trees

NOTE-:

Assignments with Different Datasets.
Business Scenerio/Group Discussion.

Module 2.13 – Time Series &Forecasting:

Time series
Estimating and Eliminating the Deterministic Components if they are present in the Model.
Estimating and Eliminating Seasonality if it is present in the Model
Modeling the Remainder using Auto Regressive Moving Average (ARMA) Models
Identify ‘order’ of the ARMA model
Forecast’ or Predict for Future Values
Practise on R

Module 2.14- Support Vector Machines – Outline

Understand when the Support Vector family of methods are an appropriate method of analysis.
Understand what a hyperplane is and how they are used with the Support Vector methods.
Identify the differences between Maximal Margin Classifiers, Support Vector Classifiers, and Support
Vector Machines.
Know how each of the algorithms determines the best separating hyperplane.
Distinguish between hard and soft margins and when each is to be used.
Know how to extend the method for nonlinear cases.

Module 2.15 – Principal Component Analysis

Understand what principal components are and when principal component analysis is appropriate.
Describe eigenvalues and eigenvectors and how they are used to calculate principal components.
Understand loading and loading vectors.
Know how to decide how many principal components to use in the analysis.
Be able to use principal component analysis for regression.

Module 3 – Python Machine & Deep Learning

Introduction to Python

Introduction to Programming
Why Python?
Why Jupyter?
Installing Python and Jupyter
Understanding Jupyter’s Interface – the Notebook Dashboard
Prerequisites for Coding in the Jupyter Notebooks
Jupyter’s Interface
Python 2 vs Python 3

Python – Variables and Data Types

Variables
Numbers and Boolean Values in Python
Python Strings

Python – Basic Python Syntax

Using Arithmetic Operators in Python
The Double Equality Sign
How to Reassign Values
Add Comments
Understanding Line Continuation
Indexing Elements
Structuring with Indentation

Python – Other Python Operators

Comparison Operators
Logical and Identity Operators

Python – Conditional Statements

The IF Statement
The ELSE Statement
A Note on Boolean Values

Python Functions

Defining a Function in Python
How to Create a Function with a Parameter
Defining a Function in Python – Part II
How to Use a Function within a Function
Conditional Statements and Functions
Functions Containing a Few Arguments
Built-in Functions in Python
Python Functions

Python – Sequences

Lists
Using Methods
List Slicing
Tuples
Dictionaries

Python – Iterations

For Loops
While Loops and Incrementing
Lists with the range() Function
Conditional Statements and Loops
Conditional Statements, Functions, and Loops
How to Iterate over Dictionaries

Python – Advanced Python Tools

Object Oriented Programming
Modules and Packages
What is the Standard Library?
Importing Modules in Python

Advanced Statistical Methods in Python

Introduction to Regression Analysis

Advanced Statistical Methods – Linear regression

The Linear Regression Model
Correlation vs Regression
Geometrical Representation of the Linear Regression Model
Python Packages Installation
First Regression in Python
Using Seaborn for Graphs
How to Interpret the Regression Table
Decomposition of Variability
What is the OLS?
R-Squared

Advanced Statistical Methods – Multiple Linear Regression

Multiple Linear Regression
Adjusted R-Squared
Multiple Linear Regression Exercise
Test for Significance of the Model (F-Test)
OLS Assumptions
A1: Linearity
A2: No Endogeneity
A3: Normality and Homoscedasticity
A4: No Autocorrelation
A5: No Multicollinearity
Dealing with Categorical Data – Dummy Variables
Making Predictions with the Linear Regression

Advanced Statistical Methods – Logistic Regression

Introduction to Logistic Regression
A Simple Example in Python
Logistic vsLogit Function
Building a Logistic Regression
An Invaluable Coding Tip
Understanding Logistic Regression Tables
What do the Odds Actually Mean
Binary Predictors in a Logistic Regression
Calculating the Accuracy of the Model
Underfitting and Overfitting
Testing the Model

Advanced Statistical Methods – Cluster Analysis

Introduction to Cluster Analysis
Some Examples of Clusters
Difference between Classification and Clustering
Math Prerequisites

Advanced Statistical Methods – K- Means Clustering

K-Means Clustering
A Simple Example of Clustering
Clustering Categorical Data
How to Choose the Number of Clusters
Pros and Cons of K-Means Clustering
To Standardize or not to Standardize
Relationship between Clustering and Regression

Advanced Statistical Methods – Other Types of Clustering

Types of Clustering
Dendrogram&Heatmaps

Mathematics

What is a matrix?
Scalars and Vectors
Linear Algebra and Geometry
Arrays in Python – A Convenient Way To Represent Matrices
What is a Tensor?
Addition and Subtraction of Matrices
Errors when Adding Matrices
Transpose of a Matrix
Dot Product
Dot Product of Matrices
Why is Linear Algebra Useful?

Deep Learning

What to Expect from this Part?
What is Machine Learning

Deep Learning – Introduction to Neural Networks

Introduction to Neural Networks
Training the Model
Types of Machine Learning
The Linear Model (Linear Algebraic Version)
The Linear Model
The Linear Model with Multiple Inputs
The Linear model with Multiple Inputs and Multiple Outputs
Graphical Representation of Simple Neural Networks
What is the Objective Function?
Common Objective Functions: L2-norm Loss
Common Objective Functions: Cross-Entropy Loss
Optimization Algorithm: 1-Parameter Gradient Descent
Optimization Algorithm: n-Parameter Gradient Descent

Deep Learning – How to Build a Neural Network from Scratch with NumPy

Deep Learning – TensorFlow: Introduction

How to Install TensorFlow
A Note on Installing Packages in Anaconda
TensorFlow Outline and Logic
Actual Introduction to TensorFlow
Types of File Formats, supporting Tensors

Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks

What is a Layer?
What is a Deep Net?
Digging into a Deep Net
Non-Linearities and their Purpose
Activation Functions
Activation Functions: Softmax Activation
Backpropagation
Backpropagation picture
Backpropagation – A Peek into the Mathematics of Optimization

Deep Learning – Overfitting

What is Overfitting?
Underfitting and Overfitting for Classification
What is Validation?
Training, Validation, and Test Datasets
N-Fold Cross Validation
Early Stopping or When to Stop Training

Deep Learning – Initialization

What is Initialization?
Types of Simple Initializations
State-of-the-Art Method – (Xavier) Glorot Initialization

Deep Learning – Digging into Gradient Descent and Learning Rate Schedules

Stochastic Gradient Descent
Problems with Gradient Descent
Momentum
Learning Rate Schedules, or How to Choose the Optimal Learning Rate
Learning Rate Schedules Visualized
Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
Adam (Adaptive Moment Estimation)

Deep Learning – Preprocessing

Preprocessing Introduction
Types of Basic Preprocessing
Standardization
Preprocessing Categorical Data
Binary and One-Hot Encoding

Deep Learning – Classifying on the MNIST Dataset

MNIST: What is the MNIST Dataset?
MNIST: How to Tackle the MNIST
MNIST: Relevant Packages
MNIST: Model Outline
MNIST: Loss and Optimization Algorithm
Calculating the Accuracy of the Model
MNIST: Batching and Early Stopping
MNIST: Learning
MNIST: Results and Testing

Deep Learning – Business Case Example

Business Case: Getting acquainted with the dataset
Business Case: Outlining the Solution
The Importance of Working with a Balanced Dataset
Business Case: Preprocessing
Business Case: Preprocessing Exercise
Creating a Data Provider
Business Case: Model Outline
Business Case: Optimization
Business Case: Interpretation
Business Case: Testing the Model
Business Case: A Comment on the Homework
Business Case: Final Exercise

Deep Learning – Conclusion

Summary on What You’ve Learned
What’s Further out there in terms of Machine Learning
An overview of CNNs
DeepMind and Deep Learning
An Overview of RNNs
An Overview of non-NN Approaches
Download All Resources

Module 4 – Tableau / MS Power BI

Module 4 a) Tableau

Module 1 – Getting Started with Tableau

Overview Of Tableau
Tableau Architecture
Installation And Configuration Of Tableau 10

Module 2 – Connecting to The Data

Managing Metadata
Managing Extracts
Data Sources
Cross-Database Joins
Data Aggregation And Data Ports
Tableau Charts
Bar Charts and Stacked Bars Data Blending
Tree Maps and Scatter Plots
Individual Axes, Blended Axes, Dual Axes and Combinational Chart

Module 3 – Visual Analytics

Drill Down and Hierarchies
Sorting, Filtering and Grouping
Trend and Reference Lines
Forecasting and Clustering
Analysis with Cubes and MDX

Module 4 – Developing First Bar Chart

Connecting Tableau to Data File
Navigating Tableau
Calculated Fields
Adding Colors, Labels and Formatting

Module 5 – Time Series, Maps and Aggregation

Data Extracts and Time Series
Understanding Granularity, Aggregation and Level of Details
Default Location in Maps
Custom Geo Coding
Symbol Map and Filled Map

Module 6 – First Dashboard

Into Section
Joining Data In Tableau
Working With Maps and Hierarchies
Scatter Plot and Applying Filters in Different Sheets
Creating First Dashboard

Module 7 – Blending Data and Dual Axis Charts

Duplicate Values
Multiple Fields
Data Blending
Dual Axis Chart
Building Calculated Fields

Module 8 – Table Calculation and Storytelling

Downloading Dataset and Connection
Mapping
Building Table Calculation For Gender
Bins and Distributions for Age
Tree Map Chart
Advanced Dashboard
Storyline and Storytelling

Module 9 – Data Preparation

Data Format
Data Interpreter
Multiple Columns And Pivot
Metadata Grid
Advanced Data Preparation

Module 4 b) MS Power BI

A Quick Introduction

What is MSPBI and its scope
Learn the common work flow in MSPBI
Building blocks of MSPBI and its relations
Quick demo how to create a business dashboard in MSPBI
MSPBI components
Old vs. new technologies
Power BI Desktop/Service/Mobile

Getting Business Data

Get data in shape for use with MSPBI
Combining two or more data sets (source data) for reporting
Tackling messy data in MSPBI
Clean and transform data

Modelling in Power BI

How to connect many different data sources
Manage data source (database) relationships
Unique keys
Calculated columns and more
Custom calculations to evaluate time-based functions
Build calculated tables based on DAX formulas and expressions
Creating and viewing visuals easier with optimized models
Discover hierarchal drill-down tools for date fields

Data Visualization

Create and customize visualization in MSPBI and its power
Use combination charts
Create and format slicers with it
Map visualizations and its utilization
Use tables and matrixes
Long live bubbles
scatter charts in action
Advanced funnel and waterfall charts
Drive fast dashboard insights with gauges and numbers
Color your visualization world with colors
shapes and scales
Adding personal touch
logo etc. to reports and dashboards
Display and present your dashboard in a way you want with summarize data
Control how your report elements overlap with each other
Learn to drill into hierarchies
Manage how levels are shared (Z-order in reports)
How to use R visuals in MSPBI

Data Exploring and Sharing

Quick insights in Power BI Service
Create and configure a dashboard
Share dashboard with your organization
Display and edit visuals- tiles
full screen
Get more space on your dashboard
Install and configure a personal gateway
Excel and MSPBI
Import and excel table into Power BI
Import excel files with data models and power view sheets
Connect One Drive for business to MSPBI
Excel data in Power BI summary

DAX (Data Analysis Expression) Application

DAX and its basic building blocks
Create calculated columns (fields) and measures in MSPBI using DAX formulas
Breadth of functions available in DAX
User variables in DAX
Create expressions across multiple tables with relational functions;
Filter and evaluate tables using advanced table functions

Please find the Data Science Course details.

Duration of Module

Data Science Course Module	Data Science Course Duration
Course Module	Course Duration
R Programming –M1	35-45 (Hours)
Python Data Science M 2	50-55 ( Hours )
Tableau / Power BI –M3	25-30 (Hours)
R & Python	80-90 (Hours)
Tableau + R + Python	110-120 (Hours)
Ad. Data Science M 1-3	125-130 (Hours)
Note: GST Applicable on all Course Module

Batch Timings

Data Science Schedule	Data Science Timing
Monday to Friday	8:00 AM – 10:00 AM
Monday to Friday	10:00 AM – 12:00 PM
Monday to Friday	2:00 PM – 4:00 PM
Monday to Friday	4:00 PM – 6:00 PM
Saturday/Sunday	8:00 \| 12:00 \| 2:00 \| 4:00
Sunday	8:00 AM – 10:00 AM
Sunday	10:00 AM – 12:00 PM
Sunday	2:00 PM – 4:00 PM
Sunday	4:00 PM – 6:00 PM

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Testimonials

Rahul Pal

From my point of view, I’ll strongly recommend SLA Consultants, Training Institute for Analyst courses. I am pursuing my Data Analyst Course from the institute under the guidance & knowledge of Data Analyst trainers, my two modules (Advance Excel and VBA) of the course has been completed. I must say, they have good training faculty, as well as have the great infrastructure for learning and practicing.

Rahul Pal

Rohit Bhagat

I strongly recommend SLA Consultants – Training Institute for doing courses. I am pursuing my Data Analyst course in SLA Institute, Head Office, till now my two modules(Advanced Excel and VBA) have been completed. Its been good experience with a trainer, as they shared me a lot of knowledge in brief. At institute the trainers and admin staff is good.

Rohit Bhagat

Deepak Sharma

I have completed Advanced Excel & Visual Basic Automation training course from SLA Consultants. I had amazing experience after completion of training program and I would like to say thanks to the trainer, who elaborate solution for all the problems very quickly at the same time with very easy Technic to better understanding. Trainers are very kind and helpful. I would like to rank him 5 out of 5. I am so much obliged and feeling grateful, thanks a ton for the great support by trainers and thank you SLA Consultants.

Deepak Sharma

MIS & Data Analyst Executive

Amit Kumar

I hope, my review may help to those who are searching for an institution where they can enhance their skills and get a placement as well. I have joined SLA Consultants for complete MIS course, my trainer is one of the best teacher of my life. Who are searching for an institute joined it immediately without hassle & wasting your time. Trainer will teach you both theoretical as well as practical knowledge, also prepare you for interviews. The best thing about trainer is that they help you even after you completion of your course, share queries on e-mail or Whats-App your query and you will get your problem resolved. My over all experience was awesome.

Amit Kumar

MIS & Data Analyst Executive

Vinay Tiwari

My experience in SLA is quite well.I have taken classes for both modules Advance Excel and VBA Macros. Whenever I raised for any doubt and to be honest my all queries and doubt were sorted out patiently. I must say my trainer was quite supportive and patiently explained everything. Even though I have taken classes on weekend basis, but never feel like that the course is running or missing anywhere. And now I am too confident about Excel and VBA after completion of training under expert trainers. I will always recommend everyone to Join SLA Consultants if they are interested in MIS and RPA etc..

Vinay Tiwari

MIS & Data Analyst

Abhishek Bharti

I have great experience in SLA Consultants for Excel and VBA Macros with expert trainers.

Abhishek Bharti

MIS Executive

Hemant Harshwal

I have joined SLA Consultants for Advanced Excel and VBA. Have gone through no. of classes and got good knowledge about Excel and VBA. Our trainer was very supportive and patiently explains every thing. He is very cooperative apart of training he also suggest regarding our career.

Hemant Harshwal

Data Analyst

Himanshu Nautiyal

I have done MIS & VBA course form SLA Consultants under the guidance of Anoop Sir, he has a very good knowledge & experience. He is a professional Trainer & his way of teaching is very good. It is great experience for me to grow with my skill & work efficiency. It is very good Platform who wants to build-up their career in MIS. Also thanks to Swati Mam for Co-ordinate with us and help me to find a good platform.

Himanshu Nautiyal

Fortune Marketing Pvt.Ltd.

Rajesh Pratap Singh

I’ve joined SLA Consultants for Advance excel and VBA It helps me to move my career. Especially Anoop Sir is very good teacher and his teaching style is awesome. One thing I want to tell you about new joiners who has no experience in Industry, they can build-up their career platform through SLA Consultants.

Rajesh Pratap Singh

Rahul Kumar

I have done MIS & VBA Training form SLA Consultants under the guidance of MR. ANOOP YADAV, he has a very good knowledge & experience in Corporate & MNC. He is professional Trainer & his way of teaching is as per the corporate requirement. I’d great experience for me to grow my skill & work efficiency. It is very good pelt from who wants to build-up their career in MIS.

Rahul Kumar

MIS Executive at ASIAN COLOUR COATED ISPAT LTD

Neeraj Gupta

SLA Consultants is too good & very helpful. If u want to achieve something in your life so please join today SLA Consultants. Especially I would like to thank Swati Mam, Anoop Sir.

Neeraj Gupta

MIS Executive at Shriram Automall India Ltd

Archit Khandelwal

Thanks to SLA consultants for giving me such type of opportunity, Great Institute with great teaching faculty. SLA is one of the class-apart institutions for those who learn something. I got a job in Fortune marketing Pvt Ltd as MIS Executive. SLA give me the confidence of ” I CAN “

Archit Khandelwal

MIS Executive at Fortune Marketing Pvt. Ltd

Anil Kumar

It was one of the best decisions to join SLA consultants for my course. The course that I pursued here was very well planned and executed. I’m highly satisfied with Anoop Sir. The lectures were easy to understand & industrial based & every doubt of mine was properly explained. I would recommend SLA consultants to all those who are willing to grow in their careers and do well.

Anil Kumar

MIS Coordinator at IQOR

Kumar Gautam

I joined MIS/VBA training at SLA. This type of practical oriented classroom training is very helpful & who’ll follow Anoop sir classes with this approach ensures that they will learn completely about subject in short period of time. “SLA Consultants” is the best for learning job-oriented classroom programs. Thanks to Anoop sir and Swati for the best placement support.

Kumar Gautam

MIS Coordinator at IQOR

Ummed Ali

SLA Consultants is better institute to learn practical training courses than any others. it provides the best training to their students. The teaching staff is very knowledgeable especially Anoop Sir for MIS and VBA courses. I learned a lot there and better career path.

Ummed Ali

MIS Executive at Evaxo pharma Pvt. Ltd

Bhavani Singh

The experience with SLA was phenomenal. It trained me to become industry ready and exposed me to various important work practices. The staff is extremely cordial and cooperative. The faculty members are the experienced and experts in the subjects taught by them. I thank SLA for the quality services and placement assistance provided by them.

Bhavani Singh

MIS Executive at G.D. FOODS MFG. (I) PVT LTD.

Anil Kumar Pal

Thanks to SLA consultants, my company values me a lot. I can churn out data from a huge database within a few minutes and keep the management happy. The ever cooperative trainers of SLA have made me capable of doing that and I will be grateful to them.

Anil Kumar Pal

MIS Executive at Recharge it Now

Priyanka Tiwari

I did not know what to do in life. Then I saw the ad of SLA Consultants. After getting knowledge of all their course offerings, I selected MIS and loved it from the start. Thank you for making my career and helping me get a good life SLA.

Priyanka Tiwari

MIS Executive at Concepts & Solutions

Santosh Kumar

When I joined, I didn’t have a clue about MIS Management and now my friends call me an expert of MIS. All thanks to SLA Consultants who filled me with great knowledge and software training without charging a lot of money.

Santosh Kumar

MIS Executive at Jai Shree Stationers Pvt. Ltd.

Kapil Negi

Data Management has always attracted me. SLA Consultants turned this hobby in a professional skill by teaching me all about MIS Management. I got a good job now and I am very happy with my life.

Kapil Negi

MIS Executive at Genpect

Aditya Pratap

I’ve completed the MIS training from SLA Consultants and now I believe that SLA provides excellent & quality training. I got placed in a reputed company with complete placement support provided the SLA placement team.

Aditya Pratap

MIS Executive at CS Datamation Research Services Pvt Ltd

Avinash

The team of trainers working with SLA is amazing. The support staff is always there to help and Arbind Sir was a great guide. I will never forget my time at SLA and I thank them for the job they helped me get after I completed my course.

Avinash

MIS Executive at Serco Global Services

Tegbahadur Singh

My love for Data Management was turned into a career opportunity by SLA Consultants. They helped me learn all latest MIS software and made me a champion of Data Management. I have a great career path thanks to the SLA Team.

Tegbahadur Singh

MIS Executive at Renovision Automation

Sushil kumar yadav

Thanks to SLA consultants, my company values me a lot. I can churn out data from a huge database within a few minutes and keep the management happy. The ever cooperative trainers of SLA have made me capable of doing that and I will be grateful to them.

Sushil kumar yadav

MIS Executive at Bharti Infratel

Rajbir Sharma

SLA Consultants played the role of a friend, philosopher and guide in my career. They helped me learn complex software and complicated terminologies in such a manner that I now excel at MIS Management. I also love the job they have helped me get.

Rajbir Sharma

Sr. MIS Executive at Computer Junction Pvt. Ltd.

Pawan Gupta

I think SLA Consultants is the best place for skill development. They made me a professional from a fresher and I have seen them do the same for hundreds of students. I highly recommend them to very student or professional seeking career growth.

Pawan Gupta

MIS Executive - Rosmerta Technology Ltd.

Anand Kumar Jha

MIS Executive - Rosmerta Technology Ltd.

Nazia Praveen

From the first day I entered the office of SLA Consultants, they helped me. I completed the admission process swiftly and started learning MIS whenever it was convenient. Now I am working in a full time job that’s not hectic but enjoyable.

Nazia Praveen

MIS Executive - Globalite Retails Pvt Ltd

Hariom Agarwal

The staff, trainers, and management of SLA Consultants is great. These people really help freshers like me to get a good job. They have changed thousands of lives and I am happy to say that mine was also changed by them. So thank you.

Hariom Agarwal

MIS Executive - Globalite Retails Pvt Ltd

Indresh Kumar

I didn’t know anything about MIS management when I joined SLA Consultants. They helped me to learn the craft of managing, sorting and presenting data and now I am a valuable asset in my company. Thank You, SLA Consultants. I will always be grateful to you.

Indresh Kumar

MIS Executive - Recharge it Now

Md Sadik

SLA Consultants played the role of a friend, philosopher and guide in my career. They helped me learn complex software and complicated terminologies in such a manner that I now excel at MIS Management. I also love the job they have helped me get.

Md Sadik

MIS Executive at Evaxo Pharma Pvt. Ltd.