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## Time Series Analysis Part I

A time series is a sequence of numerical data in which each item is associated with a particular instant in time. Many sets of data appear as time series: a monthly sequence of the quantity of goods shipped from a factory, a weekly series of the number of road accidents, daily rainfall amounts, hourly observations made on the yield of a chemical process, and so on. Examples of time series abound in such fields as economics, business, engineering, the natural sciences (especially geophysics and meteorology), and the social sciences.

• Univariate time series analysis- When we have a single sequence of data observed over time then it is called univariate time series analysis.
• Multivariate time series analysis – When we have several sets of data for the same sequence of time periods to observe then it is called multivariate time series analysis.

The data used in time series analysis is a random variable (Yt) where t is denoted as time and such a collection of random variables ordered in time is called random or stochastic process.

Stationary: A time series is said to be stationary when all the moments of its probability distribution i.e. mean, variance , covariance etc. are invariant over time. It becomes quite easy forecast data in this kind of situation as the hidden patterns are recognizable which make predictions easy.

Non-stationary: A non-stationary time series will have a time varying mean or time varying variance or both, which makes it impossible to generalize the time series over other time periods.

Non stationary processes can further be explained with the help of a term called Random walk models. This term or theory usually is used in stock market which assumes that stock prices are independent of each other over time. Now there are two types of random walks:
Random walk with drift : When the observation that is to be predicted at a time ‘t’ is equal to last period’s value plus a constant or a drift (α) and the residual term (ε). It can be written as
Yt= α + Yt-1 + εt
The equation shows that Yt drifts upwards or downwards depending upon α being positive or negative and the mean and the variance also increases over time.
Random walk without drift: The random walk without a drift model observes that the values to be predicted at time ‘t’ is equal to last past period’s value plus a random shock.
Yt= Yt-1 + εt
Consider that the effect in one unit shock then the process started at some time 0 with a value of Y0
When t=1
Y1= Y0 + ε1
When t=2
Y2= Y1+ ε2= Y0 + ε1+ ε2
In general,
Yt= Y0+∑ εt
In this case as t increases the variance increases indefinitely whereas the mean value of Y is equal to its initial or starting value. Therefore the random walk model without drift is a non-stationary process.

So, with that we come to the end of the discussion on the Time Series. Hopefully it helped you understand time Series, for more information you can also watch the video tutorial attached down this blog. DexLab Analytics offers machine learning courses in delhi. To keep on learning more, follow DexLab Analytics blog.

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## What Role Does A Data Scientist Play In A Business Organization?

The job of a data scientist is one that is challenging, exciting and crucial to an organization’s success.  So, it’s no surprise that there is a rush to enroll in a Data Science course, to be eligible for the job. But, while you are at it, you also need to have the awareness regarding the job responsibilities usually bestowed upon the data scientists in a business organization and you would be surprised to learn that the responsibilities of a data scientist differs from that of a data analyst or, a data engineer.

So, what is the role and responsibility of a data scientist?  Let’s take a look.

The common idea regarding a data scientist role is that they analyze huge volumes of data in order to find patterns and extract information that would help the organizations to move ahead by developing strategies accordingly. This surface level idea cannot sum up the way a data scientist navigates through the data field. The responsibilities could be broken down into segments and that would help you get the bigger picture.

#### Data management

The data scientist, post assuming the role, needs to be aware of the goal of the organization in order to proceed. He needs to stay aware of the top trends in the industry to guide his organization, and collect data and also decide which methods are to be used for the purpose. The most crucial part of the job is the developing the knowledge of the problems the business is trying solve and the data available that have relevance and could be used to achieve the goal. He has to collaborate with other departments such as analytics to get the job of extracting information from data.

#### Data analysis

Another vital responsibility of the data scientist is to assume the analytical role and build models and implement those models to solve issues that are best fit for the purpose. The data scientist has to resort to data mining, text mining techniques. Doing text mining with python course can really put you in an advantageous position when you actually get to handle complex dataset.

#### Developing strategies

The data scientists need to devote themselves to tasks like data cleaning, applying models, and wade through unstructured datasets to derive actionable insight in order to gauge the customer behavior, market trends. These insights help a business organization to decide its future course of action and also measure a product performance. A Data analyst training institute is the right place to pick up the skills required for performing such nuanced tasks.

#### Collaborating

Another vital task that a data scientist performs is collaborating with others such as stakeholders and data engineers, data analysts communicating with them in order to share their findings or, discussing certain issues. However, in order to communicate effectively the data scientists need to master the art of data visualization which they could learn while pursuing big data courses in delhi along with deep learning for computer vision course.  The key issue here is to make the presentation simple yet effective enough so that people from any background can understand it.

The above mentioned responsibilities of a data scientist just scratch the surface because, a data scientist’s job role cannot be limited by or, defined by a couple of tasks. The data scientist needs to be in synch with the implementation process to understand and analyze further how the data driven insight is shaping strategies and to which effect. Most importantly, they need to evaluate the current data infrastructure of the company and advise regarding future improvement. A data scientist needs to have a keen knowledge of Machine Learning Using Python, to be able to perform the complex tasks their job demands.

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## Data Driven Projects: 3 Questions That You Need to Know

Today, data is an asset. It’s a prized possession for companies – it helps derive crucial insights about customers, thus future business operations. It also boosts sales, predicts product development and optimizes delivery chains.

Nevertheless, several recent reports suggest that even though data floats around in abundance, a bulk of data-driven projects fail. In 2017 alone, Gartner highlighted 60% of big data projects fail – so what leads it? Why the availability of data still can’t ensure success of these projects?

#### Right data, do I have it?

It’s best to assume the data which you have is accurate. After all, organizations have been keeping data for years, and now it’s about time they start making sense out of it. The challenge that they come across is that this data might give crucial insights about past operations, but for present scenario, they might not be good enough.

To predict the future outcomes, you need fresh, real-time data. But do you know how to find it? This question leads us to the next sub-head.

#### Where to find relevant data?

Each and every company does have a database. In fact, many companies have built in data warehouses, which can be transformed into data lakes. With such vast data storehouses, finding data is no more a difficult task, or is it?

Gartner report shared, “Many of these companies have built these data lakes and stored a lot of data in them. But if you ask the companies how successful are you doing predictions on the data lake, you’re going to find lots and lots of struggle they’re having.”

Put simply, too many data storehouses may pose a challenge at times. The approach, ‘one destination for all data in the enterprise’ can be detrimental. Therefore, it’s necessary to look for data outside the data warehouses; third party sources can be helpful or even company’s partner network.

#### How to combine data together?

Siloed data can be calamitous. Unsurprisingly, data is available in all shapes and is derived from numerous sources – software applications, mobile phones, IoT sensors, social media platforms and lot more – compiling all the data sources and reconciling data to derive meaningful insights can thus be extremely difficult.

However, the problem isn’t about the lack of technology. A wide array of tools and software applications are available in the market that can speed up the process of data integration. The real challenge lies in understanding the crucial role of data integration. After all, funding an AI project is no big deal – but securing a budget to address the problem of data integration efficiently is a real challenge.

In a nutshell, however data sounds all promising, many organizations still don’t know how achieve full potential out of data analytics. They need to strengthen their data foundation, and make sure the data that is collected is accurate and pulled out from a relevant source.

A good data analyst course in Gurgaon can be of help! Several data analytics training institutes offer such in-demand skill training course, DexLab Analytics is one of them. For more information, visit their official site.

The blog has been sourced fromdataconomy.com/2018/10/three-questions-you-need-to-answer-to-succeed-in-data-driven-projects

## 3 Ways to Increase ROI with Data Science

In 2018, companies have decided to invest \$3.7 trillion on machine learning and digital transformation so as to embrace a promising return on that sizeable investment for professionals involved in managerial roles. Nevertheless, 31% of the companies using the potent tools of machine learning and data science are not yet tracking their ROI or are in no mood to do so in the near future.

But to be on the side, ROI is very crucial for any business success – if you fail to see the ROI you expect from data science implementation, look into bigger and complex processes at work – and adjust likewise.

Take cues from these 3 ways, explained below:

#### Implementing data science strategy into C-Suite

According to Gartner, by next year 90% of big companies would hire a Chief Data Officer, a promising role that was almost nonexistent a few years ago. Of late, the term C-Suite is gaining a lot of importance – but what does it mean? C-Suite basically gets its name from a series of titles of top level executives who job profile name starts with the letter C, like Chief Executive Officer, Chief Financial Officer, Chief Operating Officer and Chief Information Officer. The recent addition of CDO to the C-Suite has been channelized to develop a holistic strategy towards managing data and unveil new trends and opportunities that the company has been attempted to tab for years.

The core responsibility of a CDO is to address a proper data management strategy and then decode it into simple, implementable steps for business operations. Its prime time to integrate data science into bigger processes of business, and soon company heads are realizing this fact and working towards it.

#### Your time and resources are valuable, don’t waste them

Before formulating any strategy, CDOs need to ensure the pool of professionals working with data have proper access to the desired data tools and support or not. One common problem that persists is that the data science work that takes place within an organization is done on silo, and therefore remains lost or underutilized. This needs to be worked out.

Also, besides giving special attention on transparency, data science software platforms are working towards standardizing data scientists’ efforts by limiting their resources for a given project, thereby ensuing cost savings. In this era of digitization, once you start managing your data science teams efficiently, half the battle is won then and there.

#### Stay committed to success

Implementing a sophisticated data science model into production process can be a challenging, lengthy and expensive process. Any kind of big, complicated project will take years to get completed but once they do, you expect to see the ROI you desire from data science but the journey might not be all doodle. It will have its own ups and downs, but if you stay committed and deploy the right tools of technology, better outcome is meant to happen.

In a nutshell, boosting of ROI is crucial for business success but the best way to trigger it would be by getting a bird’s eye view of your data science strategy, which will help in predicting success accurately and thus help taking ROI-supported decisions.

If you are looking for a good data analyst training institute in Delhi NCR, end your search with DexLab Analytics. Their data analyst certification is student-friendly and right on the point.

## How Data Scientists are Merging Professional and Personal Resolutions for a Career Boost in 2018

The beginning of a year comes with a wide stream of promises! Some decide to work on their physique, while others look forward to visit a new country, but budding data scientists are found thinking of something else.

Here goes a chart down of what goes on in a mind of a data scientist, who could stare for hours at the computer screen pondering which code or query to run…

## How Data Analytics Influences Holiday Retail Experience [Video]

Thanksgiving was right here! Half of the globe witnessed some crazy shopping kicking off the entire holiday season, and retailers had a whale of a time, offering luscious discounts and consumer gifts at half the prices.

Before the weekend Thanksgiving sale, 69% of Americans, close to 164 million people across the US were estimated to shop– and they had planned to shell out up to 3.4% more money as compared to last year’s Black Friday and Cyber Monday sale. The forecasts came from National Retail Federation’s annual survey, headed by Prosper Insights & Analytics.

## Write ETL Jobs to Offload the Data Warehouse Using Apache Spark

The surge of Big Data is everywhere. The evolving trends in BI have taken the world in its stride and a lot of organizations are now taking the initiative of exploring how all this fits in.

Leverage data ecosystem to its full potential and invest in the right technology pieces – it’s important to think ahead so as to reap maximum benefits in IT in the long-run.

“By 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier.” – Gartner’s prediction put it so right!

The following architecture diagram entails a conceptual design – it helps you leverage the computing power of Hadoop ecosystem from your conventional BI/ Data warehousing handles coupled with real time analytics and data science (data warehouses are now called data lakes).

In this post, we will discuss how to write ETL jobs to offload data warehouse using PySpark API from the genre of Apache Spark. Spark with its lightning-fast speed in data processing complements Hadoop.

Now, as we are focusing on ETL job in this blog, let’s introduce you to a parent and a sub-dimension (type 2) table from MySQL database, which we will merge now to impose them on a single dimension table in Hive with progressive partitions.

Stay away from snow-flaking, while constructing a warehouse on hive. It will reduce useless joins as each join task generates a map task.

Just to raise your level of curiosity, the output on Spark deployment alone in this example job is 1M+rows/min.

The Employee table (300,024 rows) and a Salaries table (2,844,047 rows) are two sources – here employee’s salary records are kept in a type 2 fashion on ‘from_date’ and ‘to_date’ columns. The main target table is a functional Hive table with partitions, developed on year (‘to_date’) from Salaries table and Load date as current date. Constructing the table with such potent partition entails better organization of data and improves the queries from current employees, provided the to_date’ column has end date as ‘9999-01-01’ for all current records.

The rationale is simple: Join the two tables and add load_date and year columns, followed by potent partition insert into a hive table.

Check out how the DAG will look:

Next to version 1.4 Spark UI conjures up the physical execution of a job as Direct Acyclic Graph (the diagram above), similar to an ETL workflow. So, for this blog, we have constructed Spark 1.5 with Hive and Hadoop 2.6.0

Go through this code to complete your job easily: it is easily explained as well as we have provided the runtime parameters within the job, preferably they are parameterized.

Code: MySQL to Hive ETL Job

```__author__ = 'udaysharma'
# File Name: mysql_to_hive_etl.py
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext, HiveContext
from pyspark.sql import functions as sqlfunc

# Define database connection parameters
MYSQL_DRIVER_PATH = "/usr/local/spark/python/lib/mysql-connector-java-5.1.36-bin.jar"

# Define Spark configuration
conf = SparkConf()
conf.setMaster("spark://Box.local:7077")
conf.setAppName("MySQL_import")
conf.set("spark.executor.memory", "1g")

# Initialize a SparkContext and SQLContext
sc = SparkContext(conf=conf)
sql_ctx = SQLContext(sc)

# Initialize hive context
hive_ctx = HiveContext(sc)

# Source 1 Type: MYSQL
# Schema Name  : EMPLOYEE
# Table Name   : EMPLOYEES
# + --------------------------------------- +
# | COLUMN NAME| DATA TYPE    | CONSTRAINTS |
# + --------------------------------------- +
# | EMP_NO     | INT          | PRIMARY KEY |
# | BIRTH_DATE | DATE         |             |
# | FIRST_NAME | VARCHAR(14)  |             |
# | LAST_NAME  | VARCHAR(16)  |             |
# | GENDER     | ENUM('M'/'F')|             |
# | HIRE_DATE  | DATE         |             |
# + --------------------------------------- +
source="jdbc",
path=MYSQL_DRIVER_PATH,
driver='com.mysql.jdbc.Driver',
url=MYSQL_CONNECTION_URL,
dbtable="employees")

# Source 2 Type : MYSQL
# Schema Name   : EMPLOYEE
# Table Name    : SALARIES
# + -------------------------------- +
# | COLUMN NAME | TYPE | CONSTRAINTS |
# + -------------------------------- +
# | EMP_NO      | INT  | PRIMARY KEY |
# | SALARY      | INT  |             |
# | FROM_DATE   | DATE | PRIMARY KEY |
# | TO_DATE     | DATE |             |
# + -------------------------------- +
source="jdbc",
path=MYSQL_DRIVER_PATH,
driver='com.mysql.jdbc.Driver',
url=MYSQL_CONNECTION_URL,
dbtable="salaries")

# Perform INNER JOIN on  the two data frames on EMP_NO column
# As of Spark 1.4 you don't have to worry about duplicate column on join result
df_emp_sal_join = df_employees.join(df_salaries, "emp_no").select("emp_no", "birth_date", "first_name",
"last_name", "gender", "hire_date",
"salary", "from_date", "to_date")

# Adding a column 'year' to the data frame for partitioning the hive table

df_final.repartition(10)

# Registering data frame as a temp table for SparkSQL
hive_ctx.registerDataFrameAsTable(df_final, "EMP_TEMP")

# Target Type: APACHE HIVE
# Database   : EMPLOYEES
# Table Name : EMPLOYEE_DIM
# + ------------------------------- +
# | COlUMN NAME| TYPE   | PARTITION |
# + ------------------------------- +
# | EMP_NO     | INT    |           |
# | BIRTH_DATE | DATE   |           |
# | FIRST_NAME | STRING |           |
# | LAST_NAME  | STRING |           |
# | GENDER     | STRING |           |
# | HIRE_DATE  | DATE   |           |
# | SALARY     | INT    |           |
# | FROM_DATE  | DATE   |           |
# | TO_DATE    | DATE   |           |
# | YEAR       | INT    | PRIMARY   |
# | LOAD_DATE  | DATE   | SUB       |
# + ------------------------------- +
# Storage Format: ORC

# Inserting data into the Target table
hive_ctx.sql("INSERT OVERWRITE TABLE EMPLOYEES.EMPLOYEE_DIM PARTITION (year, Load_date) \
SELECT EMP_NO, BIRTH_DATE, FIRST_NAME, LAST_NAME, GENDER, HIRE_DATE, \
SALARY, FROM_DATE, TO_DATE, year, Load_date FROM EMP_TEMP")
```

As we have the necessary configuration mentioned in our code, we will simply call to run this job

`spark-submit mysql_to_hive_etl.py`

As soon as the job is run, our targeted table will consist 2844047 rows just as expected and this is how the partitions will appear:

The best part is that – the entire process gets over within 2-3 mins..

For more such interesting blogs and updates, follow us at DexLab Analytics. We are a premium Big Data Hadoop institute in Gurgaon catering to the needs of aspiring candidates. Opt for our comprehensive Hadoop certification in Delhi and crack such codes in a jiffy!

## 3 Data Analytics Success Stories – to Keep the Bells On

Data is the new oil – processing it into actionable intelligence is the only way of leveraging its potentials to the fullest. On this note, CIOs are courting predictive analytics tools, curating machine learning algorithms and battle-testing cutting-edge solutions to amp up business efficiencies and devise better ways to fulfill customer needs.

For robust technological growth, CIOs are investing more than ever on newer tools and systems that supports data science. Going by the research statistics of IDC, worldwide revenues in the field of big data and data analytics are expected to soar high above \$150.8 billion in 2017, an increase of 12.4%over 2016. Also, commercial purchases of software, hardware and services in pursuit of boosting big data and business analytics are expected to cross \$210 billion.

## Interesting Statistics of Employment: 5 Figures

It is a common sight to see the old and young talking about the job market that is going through a slump, regardless of the time or the economic conditions of the country; this picture usually is accompanied with some “cutting chai” at tea stalls on busy streets or cool cafes at the malls with the slurp of espresso with a tiny straw where the average upper-middle class youth talk about their first-world dreams while breathing progressive third-world air.

But is that really always the case? Data management or statistical analysis as we have established several times before, is sending the job market into hyper-drive, attracting millions of MNCs into the Indian soil and populating the job search portals with millions of opportunities in data.  But dare we only make statements, we are statisticians and we know that numbers do speak louder than simple statements.

So, in keeping with our love for figures and facts backed by data, DexLab Analytics has compiled a list of interesting statistics about the job market and the process of hiring.

#### #1 Each and every major corporate job position attracts a minimum of 250 applications!

Out of all these applications only 4 to 6 resumes get shortlisted and are called for interviews. Out of these 4 to 6 people only 1 lucky candidate is selected.

#### #2 Every job seeker takes into account 5 factors before accepting the position at a firm.

They are –

• The company culture, values and overall work environment
• Distance, ease of commute, location
• Prospects of maintaining work/life balance
• Growth prospects in career and
• Pay package and compensation.

#### #3 Almost 94 percent of sales personnel revealed that base salary is the most important determining factor in the compensation package for them.

But 62 percent of sales personnel say that commission is the most important element.

#### #4 Out of 3 employees at least 2 say that most employers do not do or do not know how to use social media platforms for promoting job openings.

And 3 out of 4 employees also believe that most companies and employers do not know how to promote their brand on social media networks as well.

#### #5 Social media platforms are used to search for jobs by 79 percent of jobseekers.

This figure rises to 86 percent for younger job seekers who are in their initial 10 years of job search.

To learn more about statistical analysis and for Data analyst certification in Gurgaon drop by our website at DexLab Analytics.