Beginner’s Guide to Data Science, The area of data science deals with many complex issues. For newcomers who want to comprehend data science simply, the accompanying complexities are frequently a barrier.

We will provide a straightforward introduction to data science for novices in this article.

Leaving aside the complexity related to data science, we will go through the specific principles in detail.

To do this, we must learn data science from scratch so that even a complete novice can grasp the idea.

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## Beginner’s Guide to Data Science

**1. What is Data Science?**

It seems like everyone is talking about data science. Very few people are aware of what a Data Scientist actually does or what the name itself means.

Data Science is a synthesis of several disciplines that draws knowledge from data and generates insights about it using scientific methods and algorithms.

We will use various anecdotes to explain data science to you. This will allow you to comprehend the fundamental ideas of data science without becoming bogged down in complicated terminologies.

Let’s begin our initial introduction to data science for beginners with this.

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**2. Data Science for Beginners â€“ Real-life Analogies**

**i. What Sells Most Chocolate?**

We use data science to comprehend trends. A data scientist’s ultimate objective is to create algorithms and employ statistical techniques to discover and comprehend patterns.

We can identify instances that “correlate” to one another by looking for patterns.

We shall learn about correlation through the example that follows. Think of a Chocolate truck that operates throughout the year, serving chocolate.

The Chocolate vendor records the total sales for the month. He notes the following things:

Month | Chocolate Sales |

January | 220 |

February | 230 |

March | 290 |

April | 320 |

May | 355 |

June | 450 |

July | 400 |

August | 380 |

September | 300 |

October | 275 |

November | 230 |

December | 200 |

Months are the independent variable (x) in the example above, and Sales are the dependent variable (y). This is due to the fact that sales depend on the calendar month.

**Do you understand where data science is going?**

The aforementioned data lead us to conclude that sales were highest in the summer and lowest in the winter.

As a result, we realize that chocolate sales follow a certain pattern. In terms of data science, we refer to the pattern in this instance as a “correlation.”

Thus, we conclude that there is a significant relationship between chocolate sales and the corresponding month of the year.

A data scientist can now create a model to forecast chocolate sales for the upcoming year using this data.

As a result, the Chocolate vendor would be more equipped to understand his industry and make decisions that would increase his sales.

**ii. Learning to Speak**

Imagine yourself in the footsteps of a baby who barely speaks a word of the language.

He/she closely imitates his parents, immersing himself in the language they use, learning the sounds’ patterns, and eventually beginning to speak it himself.

He starts to see a pattern as he notices how certain sounds consistently follow one another. This pattern aids in the child’s developing comprehension of human language.

Finding patterns is the foundation of data science. In fact, by identifying similar speech patterns, we can teach machines to understand human language.

Algorithms are created by data scientists to identify patterns in spoken language.

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**iii. Recognizing Defects**

Now put yourself close to a cereal-packaging company conveyor belt. You must identify the defective cereal packaging and take it off the conveyor belt.

You look over all the cereal boxes while imagining the ideal packaging.

You can spot a damaged packet as it travels along the conveyor belt and stop it from getting to the collection of other packets.

You also identified the pattern of typical cereal boxes in this situation and eliminated any that did not.

**iv. Using Suggestions to Make Better Decisions**

To better grasp the Data Science pattern principle, let’s look at another example. Let’s say you visit a store to buy clothes for yourself.

You locate something that appeals to you after looking through numerous different clothing trends.

The shopkeeper, who is skilled at recognizing clothing trends, displays additional items of apparel that are comparable to yours.

In this instance, the shopkeeper makes suggestions based on the pattern of your preferences.

We employ the idea of making suggestions on e-commerce websites to assist you in finding comparable goods that you have previously bought.

In order to assist you in finding things that appeal to you the most, the recommendation engine leverages data science.

**3. How Data Scientists Make Data Meaningful**

We may infer from the examples above that a data scientist must look for patterns in the data.

However, a Data Scientist must arrange the data in a standardized way before he can search for trends.

Data transformation, data cleansing, data checking for missing values, and data “normalization” are all part of this data organization process.

We will briefly go over each stage in this Data Science for Beginners tutorial.

**Extraction of Data**

The data that the data scientist extracts are frequently in a disorganized manner. Recall the first example’s observation table for chocolate sales.

In that case, the data was logically separated into two columns, month and sales.

We found it rather simple to examine and draw conclusions from that observation table. The Data Scientist, however, frequently lacks organized data.

So that he may easily examine the data and draw conclusions, he must turn it into a standard format.

**Cleaning of Data**

Data cleaning is the next task a data scientist must complete. It entails eliminating incorrect values from the supplied data. There may be values present in a given set of data that are illogical.

These factors could make it difficult for a data scientist to carry out his activities. He must thus clean the data before he can see its patterns.

**Making Up for Missing Values**

Now go back to the first Chocolate sales observation table example. Imagine that there was some information in the table that was missing, such as statistics for the number of sales in the month of August for a specific year.

A Data Scientist must be able to look for missing numbers and replace them with appropriate ones in order to thoroughly evaluate the data.

If there is a tonne of information available on the sales of chocolate over the preceding five years, the data scientist can find the average sales in August for earlier years and substitute this average for the missing amount.

Let’s say that we are missing the August sales figures for the year 2019.

However, the amount of sales in August from 2013 to 2018 is $382, $379, $380, $384, and $381. These numbers have an average of 381.20.

We, therefore, assume that there will be $381.20 worth of sales in August 2019.

If there is a tonne of information available on the sales of chocolate over the preceding five years, the data scientist can find the average sales in August for earlier years and substitute this average for the missing amount.

Let’s say that we are missing the August sales figures for the year 2019. However, the amount of sales in August from 2013 to 2018 is $382, $379, $380, $384, and $381.

These numbers have an average of 381.20. We, therefore, assume that there will be $381.20 worth of sales in August 2019.

**Normalization**

We “normalize” the data once the missing values have been replaced. We mean to scale our values in a common range when we say “normalize” or “normalization.”

We do this to ensure that the model is unaffected by the magnitude of values.

2000 mg, for instance, is a lot smaller than 20 kg. However, 2000 represents a far larger number than 20.

Scaling is therefore required to convert these numbers into a usable range.

**Tools that Data Scientists Use**

A Data Scientist uses a variety of tools to complete all of these duties. R, Python, Scala, SQL, and SAS are a few of them.

Businesses can use data science to make effective data-driven decisions. Data is becoming the lifeblood of numerous industries.

As a result, there is a great need for data scientists. One needs knowledge in a variety of disciplines, including mathematics, programming, and statistics, to succeed in the field of data science.

There is significantly less saturation compared to other IT sectors because there are so many open Data Science opportunities and so few Data Scientists.

Data Science, which is extremely adaptable, has established roots in the manufacturing, finance, consulting, and healthcare sectors.

**4. Summary**

To better grasp the true meaning of data science and the function of a data scientist, we looked at a number of cases in our lesson on data science for beginners.

We come to the conclusion that data science is the process of systematically identifying patterns in data.

Although a data scientist’s ultimate goal is always to discover relevant insights and patterns, doing so necessitates extensive data pre-processing and other crucial steps.

In the end, it is up to a data scientist to assist businesses in making data-driven decisions and improving their operations. Even so, feel free to clarify in the comments.